Analytics in Sports: Redefining the Tactics of Winning Games with Statistical Data
Sports used to be simple. Play the game and compete to win. Entertainment value aside, athletes and teams would target to compete better than the others in order to win competitions, to get those points on the board, and get their hands on the prize. The bonus- fame, money, and fans!
When it came to improving abilities, athletes and sportspersons trained hard, worked on their fitness and skills in order to reciprocate that out on the field of play. Coaches monitored, advised, and mentored them enabling the players to get better and fitter so that they could outperform their opponents. Fans followed their beloved teams and stars through various forms of media all the way from print to electronic.
When it comes to gaining a competitive edge today, training and working hard are not enough. An athlete or a sportsperson needs to know more about specific areas they need to improve, how they can maximize their strengths and minimize their weaknesses, how they can target the competition among many other areas. The necessity to identify those minute yet significant attributes and generate new metrics and key performance indicators has prompted data analytics to make its foray into sports.
Discover the applications and dimesions of sports software with Fingent. We provide Custom software and analytics solutions for the sports industry to keep track of athletic performance, to create engaging fan experiences, and with the power of analytics assist your coaching staffs with better statistics.
Analytics – The New Player
Analytics has completely disrupted the way organizations go about with their businesses by using the one commodity that globally, every industry, or every business across every domain generates: Data!
Data is what runs the show today. And it is analytics that has changed the world by using this data in infinitely creative ways to provide individuals, groups, or organizations with insight into what the data means, what information can be obtained from this data and how can this information be used to deliver positive outcomes.
What does Analytics do?
Analytics identifies this data and gathers it into a common ground so that it can be structured into information. And this information generates insights that drive business decisions. Business decisions power growth and growth defines success.
Simply speaking, analytics is a linear trajectory that empowers an individual or organization to transform itself. Analytics can be thought of as not only identifying and interpreting data but also the application of data patterns and various techniques that help in effective decision-making. Decision-makers use this information to identify past trends and make informed decisions that can have future business implications.
Consider the thought – ‘If only three out of five routes were being used by commuters to travel from New York to Boston yesterday, then improving the other two and adding two more routes could reduce travel time by 25% within the next 5 years assuming the rate of growth of traffic on the road stays constant at 15% annually’.
Analytics has changed the way we ask questions. The above example is meant to be some of the simpler scenarios of insight that one may gain using data analytics. The richness of data harnessed the power to make informed decisions. That is what analytics does.
And now, the Sports industry has a new best friend.
Analytics and Sports
While the theory of sports analytics and the study of performance statistics might have been around since the 1980s, it was hugely popularized by Billy Beane – General Manager of the American baseball team the Oakland Athletics during the late 90s and early 2000s. Some of you might be familiar with the movie or the book Moneyball. Moneyball was inspired by Billy Beane’s study of Sabermetrics or the empirical analysis of baseball statistics that measure in-game activity. Beane implemented methods to identify key performance indicators of players that would collectively bring in an improvement on his team’s performance in the long run. Using this approach, he built a competitive team that, despite having one of the lowest operating budgets out of over 30 teams in professional American baseball, consistently managed to produce performances placing them in the top five to ten teams in their league.
Global sports analysts and industry experts have remarked that the sports analytics industry is potentially expecting to reach a value of $5.2 billion dollars by 2024. Not bad for an industry that had a market value of around $125 million just about a decade ago.
Analytical approaches, however, are different when it comes to different sports. For example, baseball and cricket generate a majority of their data points during one to one interactions between players on the pitch, such as a pitcher pitching to a batter or a batsman hitting the ball towards a specific fielder. The approach used here could not be implemented in a game like Football (Soccer) or Basketball where multiple players are interacting with each other simultaneously with interactions becoming more attached among themselves if the ball happens to be closer to them.
There is no hard and fast rule when it comes to what techniques can be applied and that is where the beauty and challenges lie when it comes to applying analytics to sports.
Key areas in Sports that Implement Data Analytics Techniques
Here are some key areas in sports that implement data analytics techniques as sports organizations look to maximize performance and revenue.
1. Performance Tracking and Analysis
The seemingly minute margins are what make the difference between winning and losing. This is where analytics has helped athletes and teams improve themselves both physically and mentally.
Gone are the questions such as ‘How many goals did we score?’ or ‘How many assists does Player A have?’. Today the questions asked are more along the lines of ‘Player A has a pass percentage of 95.6 from which 47% of those passes were 15-yard passes. 34.5% of these 15-yard passes were forward passed. What percentage of those passes created a goal-scoring opportunity for our forward?’
‘Player B has made 4.5 interceptions per game over the last 25 matches out of which 80% of them happened in the middle third section of the pitch. If we can have player A close to B during these interceptions, A could then pick up the ball and make a 15-yard pass to quickly release our forwards, could he not? This ideally means that we should pair players A and B closer together in our matches.’
Once again, the examples mentioned above are some of the less complex questions that are asked with respect to utilizing analytics across sports teams. And it only gets more interesting from there.
2. Monitor and improve performance
Giving coaches and managers data ranging from the distance covered, area coverage maps, heartbeat rates to passing percentages, shot classification, positioning data, and much more allows them to analyze in great depth. Devices such as fitness bands and video cameras coupled with custom algorithms enable the recording of such data which is presented to the coaches and managers as part of a massive statistical datasheet. This gives them the flexibility to identify key statistics and use them to plan training patterns and routines.
Coaches can use this data to tailor specific training programs for their teams and individual athletes that can help them improve on key performance areas relevant to their playing style. On another front, the data can also be used to identify key improvement areas so that specific drills and exercise routines can be implemented to develop on them. Or, this data can be used to target specific weaknesses of rival players so that the team can exploit them to gain a tactical advantage.
3. Fan Engagement
The last couple of decades saw technology and digitization invading the sports industry in a way that nobody could even think of 20 to 30 years back. With smartphones and mobile apps getting so popular today, sports teams and organizations are utilizing technology to engage with their fans to provide better customer experiences, gradually increasing the marketability of sports.
Sports franchises have used mobile apps coupled with analytics to improve the experiences of match-going fans. Fans can be rewarded with discounts, ticketing offers, merchandise offers, VIP seats, or player engagement opportunities based on their attendance, seat preferences, snack preferences, and more. Fans also stream matches online today. With applications utilizing basic information to identify fan demographics such as age, location, and teams they support, sports clubs and franchises have found ways to offer personalized packages to fans that enhance their user experience.
Television and digital media are used as platforms where fans can interact not only with each other but also their players, club officials, sports pundits, and analysts as they come together to exchange ideas, opinions, and to network. Having team and player performance statistics available for visualization to the everyday fan brings out the analyst in them as well, giving them the sense of being more involved with their favorite sport, team, and heroes on the pitch – a sense of ownership if you will.
Read our case study: Legends Personal Training 3.0 – How Fingent developed a fitness regimen app for a select group of personal trainers and health professionals in Wimbledon and Kingston.
How Analytics Boosts Revenue Generation in Sports
Generating revenue is undoubtedly one of the most important aspects of running a business. This applies to sports companies too – be it running a sporting franchise, a team, or a league.
Some of the common avenues that sports teams explore include television deals for broadcasting games, ticketing for stadiums, corporate sponsorships, merchandising, and of course, player trades or transfers.
Examples of how analytics contributes to sports revenue:
- Analysts could identify leagues with potential based on past victories, player potential to attract crowds. For example, leagues, where teams have larger stadiums, would be prime candidates to earn more money through ticket sales.
- Using data, sports teams can identify the fans who attended the games, made in-stadium purchases, and the movements within the premises. This information could be pivotal for the corporate personnel as it would enable them to have a greater focus on sponsor targeting and engagement both within the stadium and outside.
- Merchandising is a key component of a sports team’s revenue-generating model. Using fan information from ticketing, fan engagement events, or even previous purchases at the club’s stores, the decision-makers could identify potential other locations to expand their reach – enabling fans to buy merchandise of their team far easier.
- Player Transfers – There are various examples of transfer fees paid by a buying club to the selling club in order to facilitate a purchase for the services of a player. How would analytics play a role in this? There are many factors that determine the ‘value’ of a player – such as his/her current form, age of the player, the relative proximity of the player’s ability in terms of current ability and peak potential, marketability of the player, and various other factors. Analysts work out algorithms using a variety of these parameters to determine optimal market values which are then used as a starting point for any player trade or sales.
The Potential of Analytics in Sports
The potential of analytics in sports is enormous. Though the sports industry has debuted analytics only lately, industry experts are trying to dive deeper into the implementation of data-driven decision making. Looking at the rising number of use cases and business benefits, it’s no surprise to say that the future of professional sports lies in the hands of sports analytics.
Fingent offers a wide array of custom software and analytics solutions for sports organizations that help them manage their basic to the most complex challenges. Get in touch with us to learn more.
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10 Services Offered by Fingent to Prepare Your Business for the Future of Digital Innovation
- Robotic Process Automation
- Business Continuity Planning
- Contactless Services
- Custom LMS | eLearning
- Business Process Re-engineering
- Reality Technologies
- Data Analytics
- Internet of Things
- Mobile Technologies
- Cloud Computing
- Innovation beyond digital transformation
It’s been more than half a year since we started battling the global pandemic COVID-19. While it shows no sign of waning and continues to dwindle economies, business leaders are still exhibiting a streak of optimism. Thanks to digital technology and infrastructure!
The American management consulting firm McKinsey states that business executives expect profits and consumer demands to rise within the next six months. However, households across the globe have reduced their spending due to low income and savings. This poses a challenge for business sectors such as banking, retail, telecom, healthcare, and utilities. The organizations belonging to these sectors have to find new ways to support customers as well as preserve shareholder value in these ambivalent times.
In spite of several unsuccessful attempts to curb the virus spread, promising vaccine tests give us hope. The good news along with it is that businesses are managing to meet their urgent requirements and are optimistic for a time when all employees can return to work safely. Business leaders need to focus on supporting their employees by building up trust and providing clear communication.
If not anything, the pandemic has taught us the necessity of digital innovation and management. The business-as-usual approach is bound to fail in these difficult times. Business leaders have to make fast decisions, use technology in new and different ways to improve productivity and accelerate digital innovation. Organizations that embrace technology and new tools to reinvent business processes will see better results. This blog enlists 10 ways by which Fingent can help you re-engineer your business processes and overcome the challenges efficiently.
1. Robotic Process Automation
In light of what’s happening, the demand for automation has fundamentally increased simply because the business dynamics have changed in various industries. Since most employees have to work from home, repetitive and data-intensive processes could be handed over to Robotic Process Automation. Once employees are freed up from performing mundane tasks, they can concentrate on other important operations that require cognitive skills.
The payroll, onboarding and offboarding processes in HR can be automated using RPA technology. In the banking sector too, RPA bots can fasten activities such as loan application processing, account opening and make them error-free.
Fingent combines technologies like Machine Learning and AI with RPA to design smarter processes for businesses. Our focus is on utilizing RPA software bots to enhance the cost-effectiveness and efficiency of business processes. We help companies to automate business processes by identifying areas that require automation, deploy the most appropriate tools, and provide the necessary maintenance and support.
2. Business Continuity Planning
A business continuity plan is crucial to ensure the effective recovery of organizations from a disaster. From conducting virtual site inspections to document collaboration to human resources and payroll management, Business Continuity Planning helps keep track of all your crucial tasks.
Fingent utilizes a wide range of technological frameworks like SAP, AWS, Microsoft technologies, public cloud services, and custom software solutions to help you come up with a strong business continuity plan. Our Business Continuity Planning (BCP) services enable you to make faster decisions, reduce costs, and retain customers. We help you create and implement a sustainable business model that allows you to keep pace with the rising customer demands.
3. Contactless Services
COVID-19 has changed customer behavior w.r.t what and how they buy. This shift has given rise to a demand for contactless services. The demand for smart deliveries, self-service facilities, and other technologies such as QR codes, mobile payments, tap-and-go are expected to rise.
Maneuvering these challenging times may seem daunting. But Fingent can help you turn the tables with our innovation capabilities and technology consulting. We guide organizations to expand their existing set of offerings and capture new markets.
Fingent helps you bring your brick-and-mortar store online by developing highly responsive, secure, and scalable ecommerce CRM solutions, customize your existing application to integrate contactless payment features, configure new offers or discount codes and also helps you leverage AR/VR technologies that allow customers to view your project sites or prototypes.
4. Custom LMS Development | eLearning
According to a recent study, 42% of organizations in the US have increased their ROI through eLearning. Over 90% of students say that they prefer eLearning to classroom training- a trend created by the COVID-19 forced lockdowns. eLearning systems are highly beneficial in times of a global pandemic like COVID-19 to ensure uninterrupted learning.
Fingent helps schools, universities, colleges, educational institutions, NGOs, and training centers to develop customized LMS platforms that come with aptitude-based smart learning tools.
5. Business Process Re-engineering
Business process re-engineering involves the fundamental rethinking and redesigning of processes to achieve significant improvements in performance, efficiency, and alignment of processes with organizational strategy. Fingent’s BPR services enable enterprises to revamp existing business processes to improve productivity, quality, and cycle time, especially during a crisis.
Here’s the difference that Fingent’s BPR brings to your table:
- Re-engineer and optimize your processes from the ground up
- Eliminate redundant processes and enhance workflow efficiency
- Coordinate and integrate multiple functions/ teams quickly
Business process reengineering offers nearly all benefits- increased revenue, improved customer service, reduced cost, higher employee retention, faster processing time. Though the risks are considerable, the crisis presents an opportunity for your business to build resilience and reshape your future roadmap by leveraging BPR.
6. Reality Technologies
The latest advancements in Augmented Reality and Virtual Reality technologies are helping businesses explore new ways to reach customers and take their innovation efforts to the next level. Perkins Coie LLP reports healthcare and medical devices as one of the top potential growth areas for AR and VR technologies. Visual-based immersive learning experiences, virtual events and conferences, virtual showings and site tours, etc. ensure sustainability, safety, and remote collaboration.
Read our case study: The Future of Communication and Security Using Augmented Reality
7. Data Analytics
The COVID-19 pandemic has disrupted supply chains and has brought about dramatic changes in customer behavior. Given the current volatility, it is nearly impossible to track real-time changes in consumer mannerisms and aptly respond to them. Fingent offers Predictive Analytics from SAP as well as other data analytics solution providers that can help you detect hidden trends and make quick decisions.
Sectors such as healthcare, retail, sports, insurance use predictive analysis in various ways. Fingent’s data analytics services guide you towards increased ROI by turning data into valuable insights.
8. Internet of Things
Business executives believe that the disruption brought on by COVID-19 will cause an increase in the demand for IoT devices. Robust 5G networks are necessary to realize the power of IoT. Join forces with Fingent to leverage the benefits of IoT in your business. Gear up for IoT with our vast portfolio of technologies that can help you create new IoT processes and manage your devices. We provide customized IoT services to meet your unique business needs. Our cloud-based IoT data management solutions help you gather insights from your IoT data.
9. Mobile Technologies
As stay-at-home and social distancing are the new norms, mobile technologies can keep business running. At Fingent, we utilize leading mobile technologies to support businesses. Being scalable and secure, our mobile solutions deliver value to your customers.
So you want to build an app, but don’t know how to get on with it? While finding the right mobile app development company may seem an obvious answer, you first need to get your requirements on paper. Fingent offers a mobile app specification template that can help you define the scope and functionalities of your app. This erases ambiguity in the development process and keeps all stakeholders on the same page.
10. Cloud Computing
The Cloud has proved to be a lifesaver for businesses whose offices are closed and employees are scattered across various locations. Those who were skeptical of the Cloud are now changing their opinions and have come to benefit from the flexibility that it offers.
Fingent’s expertise in cloud architecture models has helped many organizations realize their goals. With our proficiency in various cloud platforms such as AWS, Microsoft Azure, IBM, and Google we aim to make organizations flexible and agile. We also provide cost-effective cloud application development solutions that can be easily implemented in your current infrastructure and tailored to suit user requirements.
Innovation beyond digital transformation
As the pandemic seems to gather strength, organizations scramble to find ways to keep business running. Business leaders have to prepare for recovery by supporting their staff, establishing effective communication, and balancing costs. If nothing else, these turbulent times have brought to light the significance of digital transformation. By resorting to innovative digital technologies like the ones mentioned above, businesses can reinvent themselves and keep going.
We at Fingent focus on supporting businesses by equipping them with these smart technologies to ensure business continuity. Leveraging smart technologies, Fingent can help you understand the pulse of your customers and make the necessary changes in your business models. It has become increasingly clear that given the serious health implications on people returning to work, creating a post-pandemic organization would take longer than earlier perceived. Companies have to seize this opportunity to build new capabilities like remote work environments, virtual collaboration, automation, eLearning, eCommerce, and so on.
Our business solutions have already helped various industrial sectors solve problems and eventually succeed. Fingent can help you capitalize on this opportunity to create a digital innovation strategy. Contact us to know more.
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How Time Series Analysis Enables Businesses to Improve Their Decision Making
- Definition of Time Series
- The 5 Most Effective Time Series Methods for Business Development
- Time Series Regression
- Time Series Analysis in Python
- Time Series in Relation to R
- Time Series Data Analysis
- Deep Learning for Time Series
- Benefits of Using Deep Learning to Analyze Your Time Series
- Time Series is Valuable for Business Development
Time series analysis is one of the most common data types encountered in daily life. Most companies use time series forecasting to help them develop business strategies. These methods have been used to monitor, clarify, and predict certain ‘cause and effect’ behaviours.
In a nutshell, time series analysis helps to understand how the past influences the future. Today, Artificial Intelligence (AI) and Big Data have redefined business forecasting methods. This article walks you through 5 specific time series methods.
Definition of Time Series
Time series is a sequence of time-based data points collected at specific intervals of a given phenomenon that undergoes changes over time. It is indexed according to time.
The four variations to time series are (1) Seasonal variations (2) Trend variations (3) Cyclical variations, and (4) Random variations.
Time Series Analysis is used to determine a good model that can be used to forecast business metrics such as stock market price, sales, turnover, and more. It allows management to understand timely patterns in data and analyze trends in business metrics. By tracking past data, the forecaster hopes to get a better than average view of the future. Time Series Analysis is a popular business forecasting method because it is inexpensive.
The 5 Most Effective Time Series Methods for Business Development
1. Time Series Regression
Time series regression is a statistical method used for predicting a future response based on the previous response history known as autoregressive dynamic. Time series regression helps predictors understand and predict the behaviour of dynamic systems from observations of data or experimental data. Time series data is often used for the modeling and forecasting of biological, financial, and economic business systems.
Predicting, modeling, and characterization are the three goals achieved by regression analysis. Logically, the order to achieve these three goals depends on the prime objective. Sometimes modeling is to get a better prediction, and other times it is just to understand and explain what is going on. Most often, the iterative process is used in predicting and modeling. To enable better control, predictors may choose to model in order to get predictions. But iteration and other special approaches could also be used to control problems in businesses.
The process could be divided into three parts: planning, development, and maintenance.
- Define the problem, select a response, and then suggest variables.
- Ordinary regression analysis is conditioned on errors present in the independent data set.
- Check if the problem is solvable.
- Find the correlation matrix, first regression runs, basic statistics, and correlation matrix.
- Establish a goal, prepare a budget, and make a schedule.
- Confirm the goals and the budget with the company.
- Collect and check the quality of the date. Plot and try those models and regression conditions.
- Consult experts.
- Find the best models.
- Check if the parameters are stable.
- Check if the coefficients are reasonable, if any variables are missing, and if the equation is usable for prediction.
- Check the model periodically using statistical techniques.
2. Time Series Analysis in Python
The world of Python has a number of available representations of times, dates, deltas, and timespans. It is helpful to see how Pandas relate to other packages in Python. Pandas software library (written for Python) was developed largely for the financial sector, so it includes very specific tools for financial data to ensure business growth.
Understanding Date and Time Data:
- Time Stamps: Refers to particular moments in time.
- Time intervals and periods: Refers to a length of time between a particular beginning and its endpoint.
- Time deltas or durations: Refers to an exact length of time.
Native Python dates and times:
Python’s basic objects for working with dates and times are in the built-in module. Scientists could use these modules along with a third-party module, and perform a host of useful functionalities on dates and times quickly. Or, you could use the module to parse dates from a variety of string formats.
Best of Both Worlds: Dates and Times
Pandas provide a timestamp object that combines the ease-of-use of datetime and dateutil with vectorized interface and storage. From these objects, pandas can construct datetimeIndex that can be used to index data in dataframe.
Fundamental Pandas Data Structures to Work with Time Series Data:
The most fundamental of these objects are timetstamp and datatimeIndex objects.
- Time Stamps type: It is based on the more efficient numpy.datetime64 datatype.
- Time Periods type: It encodes a fixed-frequency interval based on numpy.datetime64.
- Time deltas type: It is based on numpy.timedelta64 with TimedeltaIndex as the associated index structure.
3. Time Series in Relation To R
R is a popular programming language and free software environment used by statisticians and data miners to develop data analysis. It is made up of a collection of libraries specifically designed for data science.
R offers one of the richest ecosystems to perform data analysis. Since there are 12,000 packages in the open-source repository, it is easy to find a library for any required analysis. Business managers will find that its rich library makes R the best choice for statistical analysis, particularly for specialized analytical work.
R provides fantastic features to communicate the findings with presentation or documentation tools that make it much easier to explain analysis to the team. It provides qualities and formal equations for time series models such as random walk, white noise, autoregression, and simple moving average. There are a variety of R functions for time series data that include simulating, modeling, and forecasting time series trends.
Since R is developed by academicians and scientists, it is designed to answer statistical problems. It is equipped to perform time series analysis. It is the best tool for business forecasting.
4. Time Series Data Analysis
Time series data analysis is performed by collecting data at different points in time. This is in contrast to the cross-sectional data that observes companies at a single point in time. Since data points are gathered at adjacent time periods, there could be a correlation between observations in Time Series Data Analysis.
Time series data can be found in:
- Economics: GDP, CPI, unemployment rates, and more.
- Social sciences: Population, birth rates, migration data, and political indicators.
- Epidemiology: Mosquito population, disease rates, and mortality rates.
- Medicine: Weight tracking, cholesterol measurements, heart rate monitoring, and BP tracking.
- Physical sciences: Monthly sunspot observations, global temperatures, pollution levels.
Seasonality is one of the main characteristics of time series data. It occurs when the time series exhibits predictable yet regular patterns at time intervals that are smaller than a year. The best example of a time series data with seasonality is retail sales that increase between September to December and decrease between January and February.
Most often, time-series data shows a sudden change in behaviour at a certain point in time. Such sudden changes are referred to as structural breaks. They can cause instability in the parameters of a model, which in turn can diminish the reliability and validity of that model. Time series plots can help identify structural breaks in data.
5. Deep Learning for Time Series
Time series forecasting is especially challenging when working with long sequences, multi-step forecasts, noisy data, and multiple inputs and output variables.
Deep learning methods offer time-series forecasting capabilities such as temporal dependence, automatic learning, and automatic handling of temporal structures like seasonality and trends.
Benefits of Using Deep Learning to Analyze Your Time Series
- Easy-to-extract features: Deep neural networks minimize the need for data scaling procedures and stationary data and feature engineering processes which are required in time series forecasting. These neural networks of deep learning can learn on their own. With training, they can extract features on their own from the raw input data.
- Good at extracting patterns: Each neuron in Recurrent Neural Networks is capable to maintain information from the previous input using its internal memory. Hence, it is the best choice for the sequential data of Time Series.
- Easy to predict from training data: The Long short-term memory (LSTM) is very popular in time series. Data can be easily represented at different points in time using deep learning models like gradient boosting regressor, random forest, and time-delay neural networks.
Time Series is Valuable for Business Development
Time series forecasting helps businesses make informed business decisions because it can be based on historical data patterns. It can be used to forecast future conditions and events.
- Reliability: Time series forecasting is most reliable, especially when the data represents a broad time period such as large numbers of observations for longer time periods. Information can be extracted by measuring data at various intervals.
- Seasonal patterns: Data points variances measured can reveal seasonal fluctuation patterns that serve as the basis for forecasts. Such information is of particular importance to markets whose products fluctuate seasonally because it helps them plan for production and delivery requirements.
- Trend estimation: Time series method can also be used to identify trends because data tendencies from it can be useful to managers when measurements show a decrease or an increase in sales for a particular product.
- Growth: Time series method is useful to measure both endogenous and financial growth. Endogenous growth is the development from within an organization’s internal human capital that leads to economic growth. For example, the impact of policy variables can be evidenced through time series analysis.
We can help you get the best of Time Series Analysis to benefit your business. Reach out to us to understand more about our data analytics and machine learning capabilities and how it can help your business grow.
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What is Exploratory Data Analysis?
Exploratory Data Analysis (EDA) is a statistical approach used to analyze data and produce descriptive and graphical summaries. Analysts may or may not use a statistical model, but EDA primarily foresees what the data can reveal to us beyond formal modeling.
With EDA you can analyze your data as it is, without the need to make any assumptions. EDA further validates and expands the practice of using graphical methods to explore data. EDA gains insights from statistical theories that give easily decipherable insights. Exploratory data analysis techniques can also be used to derive clues from data sets that are unsuitable for formal statistical analysis.
Exploratory Data Analysis displays data in such a way that puts your pattern recognizing capabilities to full use. The patterns are evident to an examination that is careful, direct, and most importantly assumption-free. Thus, you can understand relationships among variables, identify problems such as data entry errors, detect the basic data structure, test assumptions, and gain new insights.
Purpose of Exploratory Data Analysis
The prime purpose of EDA is to study a dataset without making any assumptions. This helps the data analyst to authenticate any assumptions made in devising the problem or operating a particular algorithm. Researchers and analysts can, therefore, recommend new schemes that were not previously considered.
In other words, you apply inductive reasoning to obtain results. These results may be in opposition to the theories that directed the initial data collection process. Thus, EDA becomes the driver of transformation. This approach allows you to oppose planned analyses and probe assumptions. The ensuing formal analysis can continue with better credibility. EDA techniques have the potential to uncover further information that may open new areas for research.
Role of EDA in Data Science
We need to understand the role of EDA in the whole process of data science. Once you have all the data, it has to be processed and cleaned before performing EDA. However, after EDA, we may have to repeat the processing and cleaning of data. The cleaned data and results obtained from this iteration are further used for reporting. Thus, using EDA, data scientists can rest assured that the future results would be logical, rightly explained, and relevant to the expected business circumstances.
EDA helps to clean the feature variables that are to be used for machine learning. Once data scientists get familiarized with the data sets, they may have to go back to feature engineering since the early features may be unable to serve the objective anymore. After completion of the EDA, data scientists obtain a feature set that is required for machine learning. Each dataset is generally explored using multiple techniques.
Methods of Exploratory Data Analysis
Exploratory data analysis is carried out using methods like:
- Univariate Visualization – This is a simple type of analysis where the data analyzed consists of a single variable. Univariate analysis is mainly used to report the data and trace patterns.
- Bivariate visualization – This type of analysis is used to determine the relationships between two variables and the significance of these relationships.
- Multivariate visualization – When the data sets are more complex, multivariate analysis is used to trace relationships between different fields. It reduces Type I errors. It is, however, unsuitable for small data sets.
- Dimensionality Reduction – This analysis helps to deduce which parameters contribute to the maximum variation in results and enables fast processing by reducing the volume of data.
Using these methods, a data scientist can grasp the problem at hand and select appropriate models to corroborate the generated data. After studying the distribution of the data, you can check if there’s and missing data and find ways to cope with it.
Then comes the outliers. What are your outliers and how are they affecting your model?
It’s always better to take small steps at a time. So you need to check if you can remove some features and still get the same results. More often than not, companies just venturing into the world of data science and machine learning find that they have a lot of data. But they have no clue how to use that data to generate business value. EDA techniques empower you to ask the right questions. Only specific and defined questions can lead you to the right answers.
Exploratory Data Analysis: Example with Python
Read More: Why you should migrate to Python 3
Suppose you have to find the sales trend for an online retailer.
Your data set consists of features like customer ID, invoice number, stock code, description, quantity, unit price, country, and so on. Before starting, you can do your data preprocessing, that is, checking the outliers, missing values, etc.
At this point, you can add new features. Suppose you want the total amount. You multiply quantity and unit price to get this feature. Depending on the business requirement, you can choose which features to add. Moving on, by grouping the countries and quantity or total amount together, you can find out which countries have maximum and minimum sales. Using Matplotlib, seaborn, or pandas data frame you can visually display this data. Next, by grouping the year and total amount, you can find out the sales trend for the given number of years. You can also do the same for each month and find you out which time of the year has shown a spike or drop in sales. Using this same method, you can identify further problems and find out ways to fix them.
The key to exploratory data analysis is to first understand the LOB and get a good hang of the data to get the desired answers. Get in touch with us to know more about EDA.
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Data Mining Vs Predictive Analytics: Learn The Difference & Benefits
With big data becoming the lifeblood of organizations and businesses, data mining and predictive analytics have gained wider recognition. Both are different ways of extracting useful information from the massive stores of data collected every day. Often thought to be synonyms, data mining and predictive analytics are two distinct analytics methodologies with their own unique benefits.
This blog examines the differences between data mining and predictive analytics.
Difference Between Data Mining and Predictive Analytics
Data mining and predictive analytics differ from each other in several aspects, as mentioned below:
Data mining is a technical process by which consistent patterns are identified, explored, sorted, and organized. It can be compared to organizing or arranging a large store in such a way that a sales executive can easily find a product in no time. Various reports state that by 2020 the world is poised to witness a data explosion. Therefore, data mining is a strategic practice that is necessary for successful businesses. It helps marketers create new opportunities with the potential for rich dividends for their businesses.
Predictive analytics is the process by which information is extracted from existing data sets for determining patterns and predicting the forthcoming trends or outcomes. It uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. In other words, the aim of predictive analytics is to forecast what will happen based on what has happened.
Techniques and Tools
Although there are many techniques in vogue, data mining uses four major techniques to mine data. They are regression, association rule discovery, classification, and clustering. These techniques require the use of appropriate tools that have features like data cleansing, clustering, and filtering. Python and R are the two commonly used programming languages in data mining.
Unlike data analytics, which uses statistics, predictive analytics uses business knowledge to predict future business outcomes or market trends. Predictive analytics uses various software technologies such as Artificial Intelligence and Machine Learning to analyze the available data and forecast the outcomes.
Data mining is used to provide two primary advantages: to give businesses the predictive power to estimate the unknown or future values and to provide businesses the descriptive power by finding interesting patterns in the data.
Predictive analytics are used to collect and predict future results and trends. Although it will not tell businesses what will happen in the future, it helps them get to know their individual consumers and understand the trends they follow. This, in turn, helps marketers take necessary, action at the right time, which in turn has a bearing on the future.
Data mining can be broken down into three steps. Exploration, wherein the data is prepared by collecting and cleaning the data. Model Building or Pattern Identification by which the same dataset is applied to different models, thus enabling the businesses to make the best choice. Finally, Deployment is a step where the selected data model is applied to predict results.
Predictive analytics focuses on the online behavior of a customer. It uses various models for training. With the use of sample data, the model could be trained to analyze the latest dataset and gauge its behavior. That knowledge could be further used to predict the behavior of the customer.
Data mining is generally executed by engineers with a strong mathematical background, statisticians, and machine learning experts.
Predictive analytics is largely used by business analysts and other domain experts who are capable of analyzing and interpreting patterns that are discovered by the machines.
Data mining enables marketers to understand the data. As a result, they are able to understand customer segments, purchase patterns, behavior analytics and so on.
Predictive analytics helps a business to determine and predict their customers’ next move. It also helps in predicting customer churn rate and the stock required of a certain product. Additionally, predictive analytics enable marketers to offer hyper-personalized deals by estimating how many new subscriptions they would gain as a result of a certain discount, or what kind of products do their customers seek as a complement to the main product they bought from the seller.
Related Reading: Using Predictive Analytics For Individualization in Retail
Effect of Data Mining and Predictive Analytics on the Future
The global predictive analytics market is estimated to reach 10.95 billion by 2022. We are now in a period of constant growth, where businesses have already started using data mining and predictive analytics sift through the available data for searching patterns, making predictions and implementing decisions that will impact their business.
Both approaches enable marketers to make informed decisions by increasing productivity, reducing costs, saving resources, detecting frauds, and yielding faster results. To make the best use of data mining and predictive analytics, you need the right guidance and the best expertise. Talk to our experts and find out how Fingent can help your business scale up with the power of data. Get on your way to a digital-first future with Fingent.
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Can Data Warehousing Enhance the Value of Data Visualization & Reporting?
Organizations rely heavily on data to make crucial business decisions. Hence, it is important for your business to have access to relevant data. That is where a well-designed data warehousing comes to your rescue!
Besides gaining actionable insights, corporate executives, business managers, and other end-users make more informed business decisions based on historical data.
Today’s Analytics and Business Intelligence solutions provide the ability to:
- Optimize business processes within your organization
- Increase your operational efficiency
- Identify market trends
- Drive new revenues
- Forecast future probabilities and trends
Before understanding how data warehousing can add more value to data visualization and reporting, let’s take a look at what these terms mean.
Analytics and Business Intelligence
Business Intelligence is a process that includes the tools and technologies to convert data from operational systems into a meaningful and useful format. This helps organizations analyze and develop meaningful insights to take timely business decisions. The information derived from these tools demonstrate the root cause of your business problems and allow decision-makers to strategize their plans based on the analysis.
Business Intelligence is information not just derived from a single place, but multiple locations and sources. It can be a combination of the external data derived from the market and the financial and operational data of an organization that is meaningfully applied to create the “intelligence”.
Data warehouse is a repository that collects data from various data sources of an organization and arranges it into a structured format. An ideal data warehouse set up will extract, organize, and aggregate data for efficient comparison and analysis. Data warehouse supports organizations in reporting and data analysis by analyzing their current and historical data. This makes it a core component of Business Intelligence.
Unlike a database, that stores data within, at a fully normalized or third normal form (3NF), a data warehouse keeps the data in a denormalized form. It means that data is converted to 2NF from 3NF and hence, is called Big Data.
Key benefits of a Data Warehouse
- Combine data from heterogeneous systems
- Optimized for decision support applications
- Storage of historical and current data
Why We Need Data Warehouse for Business Intelligence?
Before the business intelligence approach came into use, companies used to analyze their business operations using decision support applications connected to their Online Transaction Systems (OLTP). Queries or reports were retrieved directly from these systems.
However, this approach was not ideal due to:
- Quality issues
- Reports and queries were affecting business transaction performance
- Data resides in heterogeneous sources
- Non-availability of historical data
- Non-availability of data in the exact form required for reporting
Connecting your organization’s business intelligence tools to a data warehouse can provide you benefits in terms of production, transportation, and sale of products.
Data Warehousing and Business Intelligence Using AWS
Today, traditional BI has given way to agile BI where agile software development accelerates business intelligence for faster results and more adaptability. Big Data is growing fast to provide useful insights for making improved business decisions.
There has been a paradigm shift in data storage with warehousing solutions moving increasingly to the cloud. Amazon Redshift, for instance, is one of the most popular cloud services from Amazon Web Services (AWS). Redshift is a fully-managed analytical data warehouse on cloud, that can handle petabyte-scale data, which enables analysts to process queries in seconds.
Redshift offers several advantages over traditional data warehouses. It provides high scalability using Amazon’s cloud infrastructure to set-up and for maintenance, without the need for upfront payments. You can either add nodes to a Redshift cluster or create additional Redshift clusters to support your scalability needs.
You can use AWS Marketplace ISV Solutions for Data Visualization, Reporting, and Analysis.
Data visualization helps you identify areas that need attention or improvement, clarify factors that influence business such as customer behavior, and making decisions such as finding out a suitable market for your product or predicting your sales volumes, and much more.
TIBCO Jaspersoft, for example, is a solution that delivers embedded BI, production reporting, and self-service reporting for your Amazon data at affordable rates. It features the ability to auto-detect and quickly connect to Amazon RDS and Amazon Redshift. Jaspersoft is available in the AWS Marketplace in both single-tenant and multi-tenant versions. TIBCO Jaspersoft for AWS includes the ability to launch in a high availability cluster (HA) as well as with Amazon RDS as a fault-tolerant repository. Pricing is based on the Amazon EC2 instance, type as well as the chosen single or multi-tenant mode.
Image source: http://bit.ly/2IWWCDn
By moving your analytics and business intelligence to a hybrid cloud architecture you will be able to handle huge amounts of data and scale at the rate of expansion required by your business. You will also be able to deliver information and solutions at the speed that your employees and customers demand, and gain insights that will enable your organization to innovate faster than ever.
Business Intelligence and Data Warehousing are two important aspects of the survival of any business. These technologies give accurate, comprehensive, integrated, and up-to-date information on the current enterprise scenario which allows you to take the required steps and make crucial decisions for your company’s growth. To know how your business can benefit from the latest technologies, get in touch with our experts today
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A Comparison Between Tableau and Power BI: The Most Powerful Leaders In The BI Market.
Business Intelligence or BI tools are a precursor of the world-altering digital technology in this modern technology landscape. Analytics plays a key role in determining which Business Intelligence tool is a better choice. This is because the more flexible the analytics platform offered by a specific BI tool is, the more it provides businesses to customize applications that need updates. Let’s take a deeper look at how Power BI is different from Tableau and which technology promises a better future for your business.
Tableau And Power BI
Tableau was the first and foremost to come into the market. Though both Tableau and Power BI are well-known to be able to execute fine enough, Power BI has an advantage of making itself accessible to even the no-techy users, making it possess a higher adoption rate than Tableau.
On the other hand, Power BI is ranked higher on one of the key characteristics in terms of its Data Visualization, according to Gartner’s Magic Quadrant.
However, Microsoft’s Power BI has the most user-friendly features in terms of ‘completeness of vision’ or ‘Data Visualization’ capability and has been embedded within Office 365. But Tableau offers advanced functionality and it is best considered for power users.
So to choose a BI tool that is the best fit for your business, it is important to first learn about the analysis needs. In the recent decade, Power-BI and Tableau have emerged as the two powerful BI tools.
Let us look at how companies can choose the best for their business from the following key factors:
Cost of Tableau is on the higher side when it comes to larger enterprises. The primary reason for this premium cost is the need to build data warehousing. Thus, it is advisable for a startup to choose Power BI initially and then consider Tableau when required.
The professional version of Power BI costs you less than 10$ whereas, on the other end, Tableau would cost you more than 35$ per month per user.
Power BI supports Predictive Modelling and Reporting when on the other side, Tableau opts for Data Visualization.
With Power BI, we can create visualizations by queries and natural language. Say, for instance, Cortana PDA (Personal Digital Assistant). Power BI is said to place a 3500 limit when it comes to conducting analysis on data sets.
Tableau can be the best choice when it comes to Data Visualization. With a user-friendly dashboard, Tableau allows an in-depth data analysis. As compared with Power BI, Tableau offers more visualization flexibility.
With Tableau, we are able to create 24 different types of basic visualizations. This includes heat maps and line charts.
The functionality associated with Tableau with respect to Data Searching is on the higher side than when compared to that of Power BI.
Tableau tends to answer more queries from users as compared to Power BI.
Large Data Handling Capacities
In case of processing large chunks of data, the capacity of Tableau is over and above that of Power BI.
Power Bi handles data via import functionality and hence is slower to process large volumes of data as compared to Tableau that makes use of direct connections for the same purpose.
Tableau offers, convenience for data connectors. For example, OLAP (OnLine Analytical Processing), cloud and also big data options such as Hadoop and NoSQL. Tableau can automatically determine the relationships of data that users add from various data sources. It also provides for the creation and modification of data links manually as per the company policies.
Power BI, on the other hand, can connect to user’s external sources such as SAP HANA, MySQL and JSON. It helps users connect to third-party databases and online services like Salesforce.
Thus, if connecting to a specific data house is your business requirement, Tableau is the best choice as Power BI is integrated with Microsoft’s Azure cloud platform.
Power BI is a SAAS model. Tableau, on the other hand, is available both on cloud and on-premises options. The deployment options for Power BI is lower as some business policies do not allow for SAAS deployment. Thus, in case of flexible deployment capacity, Tableau is considered the better option here, even though it is on the higher-end when the cost factor is considered.
The user interface of Tableau allows for the creation of a customized dashboard. On the other hand, Power BI has an interface that is easy to use and intuitive. So, if easy to use is your major requisite, Power BI is the choice for your business.
Programming Tools Support
Though both Power BI and Tableau run smoothly with programming languages, Tableau can be integrated better with the R language rather than Power BI. R language provides a wide range of tools used to capture the right model of your data.
Power BI, on the other hand, also can be connected to the R language, but by using Microsoft Revolution analytics and it is made available only for Enterprise users.
Product and Customer Support
Tableau emerged in an early stage than Power BI and hence has a smaller community when compared to Tableau. The knowledge base of Tableau has three subscription categories, namely Desktop, Online, and Server.
On the other hand, Power BI offers a support functionality that is limited to users with a free account, allowing only it’s premium and pro users for faster support.
This ultimately depends on whether you want to pay the full cost up front. If yes, then Tableau should be your first choice.
If we could put it this way, Power BI can be your best choice if you are a common stakeholder because of its intuitive drag and drop features, for which a data analyst’s experience is not crucial. Tableau can win if your choice is speed and if you have the capital to support.
Related Reading: Find how SAP HANA is becoming the game changer.
In a nutshell, both Power BI and Tableau have different functionalities which depend on the variant business requirements. The best BI tool for your business can be selected only depending on the business requirements. With the help of expert IT consultants, you can make the right choice for your business. Contact Fingent today!
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5 Technology Trends Real Estate Players Should Watch Out For
Digital transformation is today the hottest area of interest in almost every business sector. With the huge growth in a digital savvy user base, enterprises big or small are continuously investing in new age digital technologies and tools to help them serve customers better and survive the tough market competition. The real estate sector is no different in this regard as digital technologies have reshaped both the customer and property landscape over the past couple of years. From facilities such as a simple listing of properties online to AI enabled best property matching based on preferences, the list of digital innovations in the real estate sector is huge. If your primary business is centered around real estate, then the following 5 technology innovations should certainly be on your watch list for 2019.
Augmented and Virtual Reality Applications
Today, real estate is a globally accessible commodity and not restrictive to localized demand. A property may attract interest from buyers across the world and not just in the vicinity or city that it is located. The challenge then lies in providing a holistic experience for the interested buyer to have a good look and feel of the property before investing. It may not always be possible to bring them physically to the place due to cost or other travel related concerns. This is where technology such as AR and VR can make a difference. With hardware devices, making AR/VR capabilities, inexpensive and accessible, property managers can easily facilitate an AR/VR demo for buyers to make faster and better decisions from anywhere. Another useful application would be for homeowners to use AR/VR to decide their home décor or construction techniques. For example, the positioning of furniture, temporary or permanent interior fixtures, etc., can be decided way before the actual construction begins. This allows more planning and elimination of cost overheads wherein both buyer and builder would have a clear understanding of how the building is to be constructed.
Check out how AR and VR are revolutionizing the real estate industry.
This video is made using InVideo.io
Big Data Analytics
Real estate is one sector where pricing is a highly dynamic constituent. There are a number of factors influencing property or land prices and very often owners or enterprises that manage properties find it hard to offer competitive or profitable pricing to prospective buyers. However, several forward-thinking players have now started to utilize the power of big data analytics to get the most preferential pricing schemes that bolster both interests from buyers as well as maintain a competitive profit margin for sellers or property owners. Historical rental or property cost data along with trends such as demographic changes, population density, preferential behavior, amenity choices, spending patterns, etc., are supplied as data insights into powerful analytical systems. These in turn offer insights or in other words recommendations for property owners to price their offerings competitively and enhance the overall buying or rental experience for a consumer.
Related Reading: Find how Big Data is influencing the health care industry.
Innovative Property Management
The rise of start-up culture and the global workforce paradigm has impacted the real estate sector considerably. Today, it is not just one major tenant that leases an entire building or space within a building to set up its office, but several players invest to take up co-working spaces. In fact, studies have shown that shared workspaces have grown at an incredible rate of 200% over 5 years from 2013 to 2018. Co-working spaces facilitate companies to set up a presence where their best employees prefer to work thereby helping in boosting retention levels. For the real estate sector, this change in occupancy trend has called for the rise of innovative property management software that incorporates several new technology offerings. For example, power and other utility services have to be shared among multiple tenants in a co-working space and this requires the property management tool to be integrated with an IoT powered building management system that can monitor occupancy levels, adjust power and other environmental support features like lighting, temperature control, etc. Doing these tasks manually would require considerable overhead and hiring of a large workforce. Autonomous IoT integrated property management solutions can easily facilitate multiple tenants to collaborate in a workspace saving them the hassle of worrying about unwanted costs and the owners can have peace of mind without having to deal with manual supply and maintenance of essential support services.
Related Reading: Check out tips to select the right software for property management.
Blockchain Powered Smart Contracts
While some of the technology advancements we mentioned before require autonomous property management, blockchain is the key ingredient that can help make it possible in real time. The decentralized and highly immutable features of blockchain can help transform real estate management and property leasing considerably. An owner or landlord can easily rent out or sell portions of his property with agreed terms and conditions all recorded on smart contracts. The contract makes the details of the sale or rental highly encrypted and complies with all local laws and regulations. Besides, several government agencies are increasingly investing in blockchain to set up regulatory repositories that can autonomously validate land records, property and tax implications and much more. So, if real estate buyers can bump up their investments in blockchain, they can turn to become more compliant and legitimate in the market. This would increase affection for their offerings for prospective customers.
Digital Advertising and Sales
Today, a large portion of prospective buyers or leasing clients are hooked to social media and other digital channels to gain information. As a real estate dealer, you need to ensure that your properties are marketed in the right digital channel and reach the right audience within such channels. There are tons of social media advertising platforms that help you filter your target audience to the finest level. If you do not want to spend time on the filtering part, there are AI-powered tools that help do the job even better than humans. AI is not just limited to ad filtering for the real estate sector. There are intelligent chatbot applications available today for deployment in your website. These bots can converse with potential buyers when they inquire on your website, collect their information, suggest them the best choices based on their preferences, pass the right information to sales agents to take the lead further and ultimately help them close it quickly. From marketing automation to intelligent sales conversations, there is a host of digital solutions that the real estate players, can utilize to win customer hearts with personalization in highly competitive markets.
Related Reading: Watch out for the emerging technology trends of 2019.
No longer can the real estate sector be labeled as a laggard in terms of technology innovations. There are plenty of digital avenues where smart players can invest and stand out from the competition. We have showcased 5 of these avenues that are increasingly driving more value for adopters and we believe that these should be on the watchlist of any real estate player in 2019. Going digital friendly is no easy task, especially if you do not have a sizeable technology team in-house who is well versed in all these innovations. But that is where a technology partner like Fingent can prove to be the decisive factor in on-boarding the best technology innovations in your real estate business. If you are aspiring to be a leader in the real estate space through technology, talk to our experts today to explore the vast opportunities in digital transformation for your business.
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IoT and data remain intrinsically linked together. Data consumed and produced keeps growing at an ever expanding rate. This influx of data is fueling widespread IoT adoption as there will be nearly 30.73 billion IoT connected devices by 2020. The Internet of Things (IoT) is an interconnection of several devices, networks, technologies, and human resources to achieve a common goal. There are a variety of IoT-based applications being used in different sectors and have succeeded in providing huge benefits to the users.
The data generated from IoT devices turns out to be of value only if it gets subjected to analysis, which brings data analytics into the picture. Data Analytics (DA) is defined as a process, which is used to examine big and small data sets with varying data properties to extract meaningful conclusions and actionable insights. These conclusions are usually in the form of trends, patterns, and statistics that aid business organizations in proactively engaging with data to implement effective decision-making processes.
Merging Data Analytics and IoT will Positively Impact Businesses
Data Analytics has a significant role to play in the growth and success of IoT applications and investments. Analytics tools will allow the business units to make effective use of their datasets as explained in the points listed below.
- Volume: There are huge clusters of data sets that IoT applications make use of. The business organizations need to manage these large volumes of data and need to analyze the same for extracting relevant patterns. These datasets along with real-time data can be analyzed easily and efficiently with data analytics software.
- Structure: IoT applications involve data sets that may have a varied structure as unstructured, semi-structured and structured data sets. There may also be a significant difference in the data formats and types. Data analytics will allow the business executive to analyze all of these varying sets of data using automated tools and software.
- Driving Revenue: The use of data analytics in IoT investments will allow the business units to gain an insight into customer preferences and choices. This would lead to the development of services and offers as per the customer demands and expectations. This, in turn, will improve the revenues and profits earned by the organizations.
- Competitive Edge: IoT is a buzzword in the current era of technology and there are numerous IoT application developers and providers present in the market. The use of data analytics in IoT investments will provide a business unit to offer better services and will, therefore, provide the ability to gain a competitive edge in the market.
There are different types of data analytics that can be used and applied in the IoT investments to gain advantages. Some of these types have been listed and described below.
- Streaming Analytics: This form of data analytics is also referred as event stream processing and it analyzes huge in-motion data sets. Real-time data streams are analyzed in this process to detect urgent situations and immediate actions. IoT applications based on financial transactions, air fleet tracking, traffic analysis etc. can benefit from this method.
- Spatial Analytics: This is the data analytics method that is used to analyze geographic patterns to determine the spatial relationship between the physical objects. Location-based IoT applications, such as smart parking applications can benefit from this form of data analytics.
- Time Series Analytics: As the name suggests, this form of data analytics is based upon the time-based data which is analyzed to reveal associated trends and patterns. IoT applications, such as weather forecasting applications and health monitoring systems can benefit from this form of data analytics method.
- Prescriptive Analysis: This form of data analytics is the combination of descriptive and predictive analysis. It is applied to understand the best steps of action that can be taken in a particular situation. Commercial IoT applications can make use of this form of data analytics to gain better conclusions.
There have been scenarios wherein IoT investments have immensely benefitted from the application and the use of data analytics. With the change and advancement in technology, there are emerging areas in which data analytics can be applied in association with IoT. For instance, actionable marketing can be carried out by applying data analytics to the product usage. IoT analytics will also allow the increased safety and surveillance abilities through video sensors and application of data analytics methods.
Healthcare is one of the prime sectors of every country and the utilization of data analytics in IoT based healthcare applications can provide breakthroughs in this area. The reduction of the healthcare costs, enhancement of telehealth monitoring, and remote health services, increased diagnosis and treatment can be achieved using the same.
The utilization of data analytics shall, therefore, be promoted in the area of IoT to gain improved revenues, competitive gain, and customer engagement. By collaborating with the right strategy partner, businesses can couple data analytics with IoT to leverage data for gaining a competitive advantage.
Monetize IoT Data with Analytics [ Source : TEDx Talks]
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Improving the customer experience is the mantra for survival in today’s highly competitive business environment. More and more businesses have identified machine learning as a reliable tool towards this end.
Machine learning is in essence software coded differently to traditional software. Rather than a long list of if-then-else statements typical of traditional software, machine learning predicts what humans would do given a specific set of inputs.
Currently, marketers and others leverage machine learning to further customer experience through improved personalization, enhancing the computer vision, improving natural language, greater decision support, through analytics optimization, and augmented analytics.
1. Machine Learning Aids Personalisation
Today’s highly pampered customers prefer and even demand personalized engagement and experiences. Machine Learning facilitates it to the hilt. Data and analytics allow marketers to understand customer preferences. Using machine learning in combination with new data sources from the Internet of Things (IoT,) telematics, geolocation beacons, and social data improve the insights.
Several marketers now apply machine learning based algorithms to understand the nuances of their customer’s preferences and engage them on their terms. Marketers use such algorithms to develop highly relevant marketing campaigns, such as a matching audience profile with highly targeted video content. These steps improve the call-to-action.
Customers receive tailored offers rather than irrelevant non-contextual offers. Such non-contextualized offers have a very low probability of conversion.
Segmentation gets better. For instance, insurance companies do not have to go by general assumptions or time-honored conventions to offer the highest automobile insurance premiums to a 16-to-25-year-old male. They can factor in everything specifically related to the customer, and tailor the premium based on individual rather than class factors.
The creation of such relevant content is the godsend at a time when over 90% of online users in the U.S. and Europe feels advertising is more intrusive today compared to two years ago.
Related Infographic: Machine Learning- Deciphering the most Disruptive Innovation
2. Machine Learning Facilitates Computer Vision
Machine Learning technology detects everything and anything, from objects and people to complex scenes within the images and videos. Applying the technology to enhance the quality of digital assets is a sure-shot way to win the customer’s heart.
One big success story is Twitter’s Magic Pony, which leverages machine learning technology to make pixelated images sharper, and enhances the quality of video captured on mobile phones in poor lighting conditions. Apart from delighting the customer, the spin-off benefit of Twitter is lower data usage, and by extension improved streaming abilities.
3. Machine Learning Aids Natural Language Processing
The next big thing revolutionizing human interactions with computers is speech recognition technology. The ability of computers to recognize human speech and act on it not only spares the hassles of keyboard typing but also unlocks a host of new possibilities. While speech recognition technology has been around for a while, the application of advanced machine learning technologies has made the system highly accurate, with error rates far lower than humans. Google’s Cloud Speech API now recognizes over 80 languages and variants, with a high level of accuracy.
Marketers can, and are leveraging advanced linguistic data and cognitive technologies spawned by speech recognition capabilities to create highly engaging content, targeted at the customer. In a sense, it furthers the cause of personalization in a big way.
Marketers benefit from natural language capabilities in myriad other ways also. A case in point is the intuitive new tool launched by Relative Insight, a UK based start-up. The tool converts natural language into data, offering marketers a wealth of information to connect with specific audiences instantly and deeply.
4. Machine Learning Improves Decision Support
Machine learning allows the marketers to predict the future. The “machine” becomes capable enough to predict the customer’s likely course of action, based on the data at his disposal, and his present behavior. The market is now flooded with several digital tools and services which provide advanced recommendations on this front.
On the anvil is “copyless paste,” where machine learning will save users time by proactively offering to share information between apps. Marketers will leverage the concept further to offer proactive product suggestions. Integration with other systems also offers the scope for proactive and automatic delivery.
5. Machine Learning Facilitates Analytical Optimization
Businesses leverage the immense analytics opportunities offered by machine data to fine-tune their operations, deliver new business models, and offer new products and services in tune with customer demand. The insights gained, predict not just how a customer may behave or act, but also how the competition may move in the future.
One sector where machine learning algorithms are already in widespread use is the financial sector. Financial services companies use various machine learning algorithms such as random forest and gradient boosted models for a host of applications, from predicting the probability of being ranked at the top of aggregator portals to predict midterm cancellation rates on policies, and more. These applications have a direct bearing on customer satisfaction. For example, banks and financial institutions predict volumes for credit card lines, to adjust rates and terms, and thereby attract the right type and volume of customers for the specific product.
Related Reading: Top Artificial Intelligence Trends to Watch Out for In 2019
6. Machine Learning Facilitates Augmented Analytics
The scope of machine learning improves with the development of technology. Neural networks support better classification and forecasting, decision trees support more complex rule and relationship-based customer experience programs. All these improve the organization’s ability to support complex decisions, forecasts, and optimizations.
Augmented analytics, which co-opts these latest and emerging technologies, combines various elements of the ecosystem, such as data preparation, business intelligence, predictive analytics and machine learning capabilities into a single, automatic and seamless process. Enterprises would be able to cleanse their data easily, to uncover latent insights and patterns.
Today’s huge data create millions of variable combinations impossible to process manually or even with traditional tools. Augmented analytics, powered by machine learning, deliver quicker insights, reducing customer frustration.
What exists now is just the tip of the iceberg. The future holds a world of possibilities. A case in point is the fragmented nature of the machine learning ecosystem being all set for a big churn. Increased competition, the hyper-fast paced changes in technology, and the proliferation of big data at an alarming frequency force many open source machine learning libraries, algorithms and frameworks to join forces and deliver a better deal to their customers. The lower-level personalization commonplace today will make way for a more robust collaborative filtering, delivering a much higher degree of personalization and contextualization than present levels.
Side-by-side, the machine learning ecosystem is becoming increasingly easier to use, and more affordable. Hitherto, only enterprises with large analytics teams could really afford to play around with machine learning. The advent of various solutions delivered in a cloud-based subscription model makes the power of machine learning available to the masses, including start-ups, freelancers, and even individuals.
Marketers and brands can leverage the improved ecosystem to generate a better picture of their customers’ true context, and serve them better. Simply put, customers will get better food, movie, music, travel, product and purchase recommendations.
Related Reading: AI To Solve Today’s Retail Profit Problems