Tag: data

Undoubtedly, data is what we see almost everywhere, and it is enormous. And it doesn’t stop there, it is growing continuously at a level beyond imagination! Let’s have a look at how it has changed over the years.

A look into how Data and AI transformed in years!

In the 1950s, when there were fewer technological developments, companies would collect the data(offline) and analyze it manually. This was also backed by limited data sources that made it time-consuming in obtaining the results.

The mid-2000s paved the way for changing the world for the better and it was during this time the term “big data” was coined. Almost every business that had something to do with digital infrastructure started looking for ways to use the large data and come up with meaningful insights.

This era also saw the invention of tools like Data mining, OLAP, etc., taking technological advancements to the next level. In general, the internet gained immense popularity not only for organizations but also for households. During this time, technology became more advanced and provided automated options for managing data, and data analysts could analyze data, trends, etc., and provide better recommendations.

Google, Amazon, Paypal, and others also made a mark causing the volume of data to reach newer heights. However, all this posed a storage and processing problem.

The late 2000s to early 2010s saw a surge in Facebook, Twitter, Smartphones, and connected devices. The companies used improved search algorithms, recommendations, and suggestions driven by the analytics rooted in the data to attract their customers. Enterprises also realized that would have to deal with unstructured data and so they got familiar with databases such as NoSQL. New Technologies were introduced for faster data processing and machine learning models were used for advanced analytics.

Now, businesses are a step ahead and using automated tools using cloud and big data technologies. With cloud platforms, it is now easier to enable massive streaming and complex analytics.

Read more: 5 ways in which big data can add value to your custom software development

Having seen how data has evolved over the years, let’s have a look at how Artificial Intelligence has transformed in the last generation.

In 1950, a British mathematician and WWII code-breaker- Alan Turing was one of the first people to come up with the idea of machines that could think. To date, the  Turing Test is used as a benchmark to determine a machine’s ability to think like a human. While this notion was ridiculed at the time, the term artificial intelligence gained popularity in the mid-1950s, after Turing’s death.

Later, Marvin Minsky, an American cognitive scientist picked up the AI torch and co-founded the Massachusetts Institute of Technology’s AI laboratory in 1959. He was one of the leading thinkers in the AI field through the 1960s and 1970s. It was the rise of personal computers in the 1980s that sparked interest in machines that think.

That said, it took several decades for people to recognize the true power of AI. Today, Investors and physicists like Elon Musk and Stephen Hawking are continuing the conversation about the potential for AI technology in combination with big data could have and how it could change human history.

AI technology’s promising feature is its ability to continually learn from the data it collects. The more the data it collects and analyses through specially designed algorithms, the better the machine becomes at making predictions.

Impact on business

AI and big data have an impact on businesses like never before. Whether it is workflow management tools,  trend predictions, or even advertising, AI has changed the way we do business. Recently, a Japanese venture capital firm became the first company ever to nominate an AI board member for its ability to predict market trends faster than humans.

On the other hand, data has been the primary driver for AI advancements. Machine learning technologies can collect and organize a large amount of data to make predictions and insights that otherwise cannot be achieved with manual processing. This not only increases organizational efficiency but reduces the chances of any critical mistake. AI can detect spam filtering or payment fraud and alert you in real-time about malicious activities.

AI machines can be trained to handle incoming customer support calls thereby reducing costs. Additionally, you can use these machines to optimize the sales funnel by scanning the database and searching the Web for prospects that have similar buying patterns as your current customers.

Read more: The Future of Artificial Intelligence – A Game Changer for Industries

5 trends in data and artificial intelligence that can help data leaders.

1. Customer experience will be the key

Supply chain and operating costs will mean nothing if you are unable to hold on to your customers. Today, businesses have to be more connected with their customers to be on top of the game. From in-person and digital sales to call centers, companies will have to collect data to have a holistic view of the customer. Businesses must consider other forms of interaction such as using voice analytics to understand how customers interact with call centers or chatbots.

2. Leveraging External data

External data can provide early warning signs about what’s going on. To make external data work, companies must start with a business problem and then think about the possible data that could be used to solve it. That said, companies might need to modernize data flows to leverage external data.

While many businesses have started leveraging external data, some companies haven’t leveraged it yet as they are either too focused on internal data or finding it difficult to transfer data.

A prime example of brands that used external data is Hershey’s Chocolates. It leveraged external data to predict an increase in the number of people using chocolate bars for Backyard S’mores and a decline in sales for smaller candy bars for trick-or-treating.

3. CDOs leading the way towards a data-driven culture

Introducing any new technology without training your employees to adapt and figure out new skills and processes will not be effective. According to Cindi Howson, chief data strategy officer at analytics platform provider ThoughtSpot, Chief Data Officers (CDOs) need to take the lead and empower their employees and the organization to gain time and efficiency with data.  Also, CDOs will have to make sure to upskill employees to take full advantage of new technology.

4. Multi-Modal learning

With advances in technology, AI can support multiple modalities such as text, vision, speech, and IoT sensor data. All this is helping developers find innovative ways to combine modalities to improve common tasks such as document understanding.

For example, the data collected and processed by healthcare systems can include visual lab results, genetic sequencing reports, clinical trial forms, and other scanned documents. This presentation, if done right, can help doctors identify what they are looking at. AI algorithms that leverage multi-modal techniques (machine vision and optical character recognition) could augment the presentation of results and help improve medical diagnosis.

5. AI-enabled employee experience

Business leaders are starting to address concerns about the ability of AI to dehumanize jobs. This is driving interest in using AI to improve the employee experience.

AI could be useful in departments such as sales and customer care teams that are struggling to hire people. Along with robotic process automation, AI could help automate mundane tasks to free up the sales team for having a better conversation with customers. Additionally, it could be used to enhance employee training.

Read more: 9 Examples of Artificial Intelligence Transforming Business Today

Conclusion

Leveraging data and Artificial intelligence has grown due to the pandemic and businesses are digitally connected than before the lockdown.

At Fingent, we equip business leaders with insights, advice, and tools to achieve their business goals and build a future-proof organization. To learn more about how we fuel decision-makers to build successful organizations of tomorrow, contact us.

Streamlining SAP S/4HANA Migration with Selective Data Transition

Though the COVID-19 pandemic accelerates digital adoption among industries, the journey towards Intelligent Enterprise can be complex. Typically, this begins with establishing an intelligent core like SAP ERP. However, even migrating to a new system is more than just a simple upgrade for many companies. With several years of historical business data across complex system landscapes, companies will have to adopt a specific migration approach to SAP S/4HANA. A well-defined migration approach will help businesses ensure business continuity, embrace innovation, and stay relevant and competitive. This post will look at SAP S/4HANA Selective Data Transition Engagement and how this approach accelerates SAP S/4HANA migration.

Read more: Deploying SAP S/4HANA: Tools and Methodologies to Adopt

Two Popular SAP S/4HANA Migration Methods

There are two popular methods to manage SAP S/4HANA migration.

1. Greenfield migration

The first option is the Greenfield approach or a complete re-engineering (new implementation). However, the limitation of this approach is that you cannot migrate your historical data and so cannot take advantage of the latest intelligent features you need for your company’s historical data to work.

2. Brownfield migration

The second option is the Brownfield approach that allows migrating high volumes of historical data (system conversion). However, moving a vast amount of historical data could leave you with more data that could be non-compliant with data privacy laws. Also, this approach could leave you with a lot of irrelevant data in your newly configured system.

As both these options offer certain limitations, SAP formed a brand-new approach, the SAP S/4HANA Selective Data Transition Engagement, that makes your data migration to SAP S/4HANA a lot easier.

What is Selective Data Transition?

Selective Data Transition, also known as Hybrid Approach or Landscape Transformation, is an alternative to the New Implementation approach (Greenfield) or System Conversion approach (Brownfield). It allows you to consolidate several ERP systems to one central SAP S/4 HANA system. Selective Data Transition is vital for companies moving from an existing SAP ERP solution to SAP S/4HANA on-premise or SAP S/4 HANA cloud (private edition).

Just like an inland river delta flowing into the ocean, Selective Data Transition allows you to migrate only a relevant selection of your existing ERP data to SAP S/4 HANA. So, if you’re on SAP ECC planning for transition to SAP S/4HANA, then selective data transition is one of the best approaches to move to SAP S/4HANA.

Case Study: Thermo Pads partners with Fingent to migrate from ECC 6 to SAP S/4HANA. View Case Study

Business Benefits of Selective Data Transition Approach

• Selective data transition avoids business disruption when moving to SAP S/4HANA and enables you to go live according to your business needs. In general, this approach allows for a single go-live, moving several organizational units or roll-out in multiple phases, for example, by country.
• This approach enables you to migrate only your relevant historical data and retain a consistent process chain while leaving behind obsolete data, for example, outdated company codes.
• Selective data transition helps you define your speed and combine single projects such as a new go-live implementation, finance management, etc. while moving to SAP S/4HANA in a single step or a phased approach.
• With SAP S/4 HANA, you can introduce new business processes and manage your historical data while protecting good practices and previous investments.
• The near-zero downtime approach restricts the technical downtime to just a few hours.
• Selective data transition can help change your landscape by either splitting or consolidating existing systems.

Read more: 7 Tips to Ensure a Seamless Transition to SAP S/4HANA

Customize Your Selective Data Transition Approach with Fingent

Selective Data Transition Engagement is a great way to balance between redesign and reuse. It can happen across various scenarios for companies of different sizes to support a smooth transition to SAP S/4HANA. This approach provides a suitable IT landscape for every company’s unique needs. Selective data transition goes beyond the standard implementation as it offers several options for customers to choose from, based on their current landscapes and future needs.

You can work with Fingent and define the appropriate structural changes that need to be considered during data migration, along with any business processes that may need to be modified or redesigned. Our tailored approach has helped many of our customers to increase their flexibility to adapt data and processes in just one step.

Read more: SAP S/4HANA journey: 8 ways C-level leaders and executives can derive business value 

With Selective Data Transition Engagement, companies can even take advantage of select innovation and modify their business processes to facilitate the use of the latest tools and functionality without the need to start from scratch.Companies having mature SAP landscapes can clean up their environments while retaining those investments that matter most and lower their transitions by bypassing preparation projects.

Selective data transition can even help split some of your projects between the cloud and on-premise. It also allows you to restructure and consolidate your environments as you want. But, most importantly, this approach helps you save time by combining all tasks in just one step.

Read more: 6 capabilities of SAP S/4HANA that will help you become an intelligent enterprise 

So, if you are interested in learning how the SAP S/4HANA Selective Data Transition Engagement can help your organization accomplish successful migration, don’t hesitate to contact Fingent. We are an SAP Silver Partner and have the expertise in this area to expedite your intelligent enterprise journey.

Supercharge Your Business with These 11 Hot Tech Trends

Technology is having an ever-greater impact on our personal lives and most importantly on the way we do business. The business world has transformed rapidly in the past few years and it will evidently continue to change the pace of business in the years to come. Whether it is the production of goods or the computing devices used at the office, new technologies have helped businesses run smoother and more effectively. As information travels more quickly and reliably, businesses are realizing how easy it is to grow globally and across multiple sectors. To gain benefits, however, businesses must keep up with technology and adopt new trends. This article discusses 11 tech trends to look forward to in the next couple of years. First, let us understand what drives these tech trends, and then we will consider their impact on businesses.

What Will Drive the Evolution of Tech Trends?

Two major reasons for the evolution of all kinds of tech trends are our day-to-day business challenges and the passion for innovation. As an intelligent being, it is normal for human beings to innovate to live better. However, from a business standpoint, it is about optimizing all that humans are capable of accomplishing and that includes making profits. 

1. Greater predictive levels and programmability will reshape cybersecurity

Cybersecurity will be one of the prominent IT functions to mature. New technologies will bring about a fundamental shift in cybersecurity as they enable greater predictive capabilities and programmability. It will become more predictive with the use of large-scale data along with AI, analytics, and machine learning. As a general rule, the more we have, the more difficult it becomes to protect it from thieves. That is not the case with immense data accumulation. Even as data increases it will become easier to determine and match patterns to both predict and shut down any attack vectors. 

As every element of infrastructure becomes programmable, you can put a firewall inside the software of all virtual machines in your architecture, thus limiting the flow of data within the software. This will eliminate the need of storing data on a network from where the data can easily be hacked or stolen. Businesses will continue to see this capability emerge as programmability improves. 

Read more: Safeguarding IT Infrastructure From Cyber Attacks – Best Practices

2. Promise of serverless computing 

According to IDC’s prediction, “from 2018 to 2023 — with new tools/platforms, more developers, agile methods, and lots of code reuse — 500 million new logical apps will be created, equal to the number built over the past 40 years.” Currently, we are witnessing a distinct change in the application infrastructure with most businesses moving to cloud-native applications. 

There are four distinct computing models that are evolving simultaneously such as virtual servers, physical servers, container-based computing, and serverless computing. Virtual servers and container-based computing make it easy to move applications. Whereas the promise of serverless computing offers greater agility and cost savings as the applications do not need to be deployed on a server. Alternatively, functions can be run from a cloud provider platform. It can return outputs and instantly release the associated resources.  As businesses see a change in the infrastructure, they will have to make choices about how they approach development. 

Read more: 5 Trends That Will Transform Cloud Computing in 2020 

Accelerated in part by the long-term shutdown due to COVID-19, industries that design and manufacture products will quickly adopt cloud-based technology trends to aggregate and intelligently transform. In a couple of years, these intelligent algorithms will allow manufacturing assembly lines to optimize towards increased levels of output and enhanced product quality thereby reducing the overall waste in manufacturing by half.

3. IoT and Edge

IoT and Edge can rightly be termed as superpowers of the tech world. The developing, managing, and running of widespread IoT and Edge applications will grow in complexity with numerous endpoints.  For example, audiovisual technologies are being used to achieve the same input as you would get when you connect numerous IoT sensors. When compared to individual sensors, these tech trends provide reasonable mass coverage at a minimum cost. This is only possible when AI and ML are integrated into the IoT platform. Powering and maintaining thousands of sensors is a daunting prospect. Audiovisual solutions can thus make this a significant growth area. As a result, these changes will have a profound impact on how the businesses get value out of new data that they are able to collect and process.

Read more: Gearing up for IoT in 2020 

4. Everything will revolve around data 

Enormous amounts of data collected from IoT devices and digital platforms can now be made available through application programming interfaces for business insights, analysis and to develop other applications. Collecting information from a sufficient amount of data-points enables you to model behavior and understand patterns and come to more accurate conclusions quickly with minimum cost. With abundant data from multiple touchpoints and new analytic tools, businesses are able to customize products and services by creating ever-finer consumer microsegments. Businesses that do not build around data will find themselves swamped by its enormity. 

5. Voice control is the next evolution of human-machine interaction

The advent of voice technology such as Apple’s Siri, Google’s Assistant, and Amazon’s Alexa is disrupting businesses as it creates a three-way interaction between devices, services, and people. It has completely changed the way consumers interact with smart devices. 

According to recent research, by 2024, the global voice-based smart speaker market could be worth $30 billion. This technology trend has a huge impact on how online searches will happen. Businesses will have to adapt their way of promoting their products and services. It will also affect the way companies are organized as internal knowledge can be shared more easily which improves the possibility of multitasking. This will result in increased productivity. 

In the next couple of years, we will see a transformation of voice technology from being an information tool to a transaction tool. It offers the possibility to directly order from brands and perhaps even pay.

Read more: 6 Key Predictions for AI-Driven Voice Computing in 2020 

6. Blockchain platform market

Blockchain started as an offshoot of the cryptocurrency movement. Now, it is evolving to find use cases beyond just the international settlement areas. According to Gartner, Blockchain will grow to slightly more than $176 billion by 2025 and continue to exceed $3.1 trillion by 2030. The marriage of Blockchain and Artificial Intelligence can significantly change the nature of transactional businesses. This is possible as Blockchain is a decentralized unchangeable space for encrypted data and AI will assist you in analyzing and interpreting that data quickly and reliably to drive actionable insights. There is great potential for this technology to have an immense impact on cybersecurity. 

Read more: How Blockchain Enables the Insurance Industry to Tackle Data Challenges 

7. Seamless blend of the digital twin

Applied technology will intersect between the physical and digital worlds, the digital twin. For example, the digital twin will have a perfect digital copy of the physical world.  Applied technology will allow you to blend these two worlds seamlessly. The resulting immersive environments will have a pervasive impact on the industry. This twin will allow you to collaborate virtually, simulate conditions quickly, understand what-if scenarios clearly, predict results more accurately, and more. Most businesses are already aware of the benefits of applying the digital world to enhance the physical world. They are digitizing physical processes to reduce inconsistencies, redundancies, and human error.

Explore: Learn more about Digital Twin Technology 

This pandemic has shown us that communication is not just for work but is required to form real emotional connections. In the next couple of years, AI technology will be used to connect people at a human level and drive them closer to each other. There has been a lot of concern over the security of video conferencing companies. However, these concerns will move companies to ensure that they provide secure digital connectivity for their consumers. 

Being a secure video conferencing software, InfinCE has been a game-changer for enterprises of all sizes. Click here to explore

8. 5G will be the game-changer

5G has innumerable use cases beginning from healthcare to more reliable security. With 5G, the audio-video experience will be faster and clearer than it has ever been. On the flip side, 5G will enable businesses to provide remote opportunities for their employees with work experience that would be similar to that inside the office. It will bolster recruitment and retention efforts for top talent. 

As more businesses move their critical tasks to the cloud, employees will become increasingly productive from wherever they work and with whatever device they use. Though currently, 5G coverage is limited, according to Ericson’s Mobility report, 5G subscriptions could cover up to 65% of the world’s population by 2025. Businesses that anticipate and embrace these emerging technology trends will see a positive impact in the years to come. Low latency 5G networks can help resolve the challenges caused by the absence of reliable networks and can facilitate more high-capacity services. Private 5G networks can offset the high cost of mobility with economy-boosting activities. 

Read more: From Remote Work to Virtual Work, 5G is Reinventing the Way We Work 

9. Data lakes enable new analytic models

Data lakes are storage repositories that contain quantitative and qualitative data. Data lakes enable new models of predictive analytics and help unlock the potential of digital twins. Since they can hold enormous amounts of data, organizations can leverage the insights, including discrete data points to create a ‘digital twin’ of each customer. You can gain access to customer details such as demographic data, browsing behavior, purchasing patterns, and payment preferences. The ability to gauge qualitative data will increase the demand for robust ERP systems and AI-driven automation. This would mean that businesses should acquire the skills to set up, manage, and secure their data lakes and build data models that will help extract the insights they require for ongoing innovation. 

Read more: 7 Key Differences Between Data Lake and Data Warehouse 

10. Sophisticated sentiment analysis for real-time insights

Sentiment analysis uses techniques to interpret and classify the ‘mood’ of your customers. Sophisticated sentiment analytical tools allow businesses to recognize the customers’ sentiment towards a product, a service, or a brand. It can also be used by the businesses to respond to the feedback with a proactive approach. It allows businesses to understand how people are feeling in real-time and proactively position products, services, and visual merchandise. In the future, this technology will be used in addition to tools such as conversation intelligence, text analysis, and natural language processing. It can enable innovation on demand. Businesses will find it advantageous to incorporate sentimental analysis into their data analysis in the areas of customer feedback, marketing, CRM, and e-commerce.

Read more: CTOs Guide – How Robotics and AI Can Improve Customer Experience 

11. Micro-fulfillment for e-commerce fulfillment 

Robotics has turned around numerous industries except for a few sectors such as grocery retail. With the new robotic application termed micro-fulfillment, grocery retailing will no longer remain the same. Micro fulfillment allows you to convert personal garages into storage spaces and can operate 5-10% more economically than a brick and mortar store. This rising trend is captured in tiny, urban warehouses that leverage high-end automated systems to complete online orders with greater efficiency. These centers are used to deliver goods rapidly, in as fast as an hour. Robotic arms can be used to pick up items. The application of robotics downstream at a ‘hyper-local’ level will disrupt the grocery retail industry. This technology trend will unlock wider access to food and a better customer proposition such as product availability, speed, and cost. 

Read more: 5 Transformative Trends Ushered by B2B E-commerce in Healthcare and Life Sciences 

How Technology will Continue to Disrupt Businesses?

The transformative potential of innovative technology trends is exciting businesses today. It will change the way businesses plan, start, manage, operate, market, and make a profit. The next couple of years will see profound improvements in addressing most business challenges as organizations develop and deploy solutions that will deliver tangible results. Driverless cars, 3d printing, artificial and business intelligence tools, robotics, and IoT are just a few examples of how technology has transformed or disrupted the business world and has the potential to continue to disrupt. 

The COVID-19 pandemic has necessitated worldwide collaboration, transparency of data, and speed at the highest levels to navigate the human and business impacts. Now is the time to recognize and support the opportunities for technology trends that can best and most rapidly address business challenges. Partner with us to capitalize on these trends and scale your business quickly. 

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. 

Read more: How Cognitive Computing is Revolutionizing the World of Sports

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?’ 

or 

‘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.

Read more: iBeacon Technology in Sports and Other Industries

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. 

Data Lake vs Data Warehouse

A data lake is a location where new data can enter without any hurdles. Since any kind of data can reside in a data lake, it is a great source to unearth new ideas and experiment with data. However, due to this openness, it suffers from a lack of meaningful structure. The larger business audience may find that the data lake is a mess. This is where the scalability traits of the data warehouse gain significance. In data warehousing, we try to match dimensions and measures into queryable components that are consistent. This makes it easier for an ever-scalable audience to consume this data. 

Let us now take a deep dive and compare the properties of a data lake and a data warehouse.

Read more: How Data Warehousing Adds Value To Data Visualization & Reporting

7 key differences between data lake and data warehouse

1. Type of Operation:

• A data warehouse is used for Online Analytical Processing (OLAP). This includes running reports, aggregating queries, performing analysis, and creating models such as the OLAP model based on whatever you want to do. These operations are carried out typically after your transactions are done. For example, you want to check all the transactions done by a particular client. Since the data is stored in a denormalized format, you can easily fetch the data from a single table and showcase the required report.
• A data lake is used typically to perform raw data analysis. All the raw data i.e XML files, images, pdf, etc. are just gathered for further analysis. While capturing data, you don’t have to define the schema. You may not know how this data can be used in the future. You are free to perform different types of analytics to uncover valuable insights.

2. Cost of storing data:

• In data warehouses, the cost of data storage is high. This is because the software used by these data warehouses are expensive. Additionally, the cost of maintenance is also high since it consists of power, cooling, space, and telecommunications. Another point to consider is that since a data warehouse contains large amounts of data in a denormalized format, it tends to take up a lot of disk space.
• Contrarily, in data lakes the cost of data storage is low. They use open-source software which costs less. Also since the data is unstructured, data lakes can scale to high volumes of data at low cost. 

3. Schema:

• Data warehouses use schema-on-write. Before storing the data, it has to be transformed and provided for application in analytics and reporting. You need to know for what purpose you’ll be using the data prior to importing it into the data warehouse. As new requirements arise, you may have to reevaluate the models that were defined earlier.
• On the other hand, data lakes employ schema-on-read. Without the necessity of a single schema, users can store any kind of data in the data lake. They can discover the schema later while reading the data. This means different teams can store their data in the same place without relying on the IT departments to write ETL jobs and query the data.

4. Data Quality:

• A data warehouse contains high-quality data. As the data undergoes extreme curation before storage, it can be considered as the central version of the truth. 
• A data lake contains raw data that may or may not be curated.

Read more: Data Visualization vs. Data Analytics – What’s the Difference?

5. Users:

• Typically business professionals who deal with reporting use data warehouses. Again, since the operation costs of a data warehouse tend to be higher, large and established organizations that deal with tons of data opt for it. 
• Data scientists and analysts generally use data lakes. With raw data the possibilities are endless. They can perform various types of analytics to glean insights and identify patterns to convert the data at hand into valuable information. 

6. Security:

• Data warehouses tend to store extremely sensitive data for reporting purposes. These could be compensation data, credit card information, healthcare data, and so on. The data security for data warehouses is mature and robust since this technology has been around for quite a while now. Only authorized personnel can access the data warehouse.  
• Data Lake is a relatively new technology and hence data security is still evolving. As mentioned, a data lake is created using open source technologies. Therefore its data security is not as great as that of a data warehouse.

7. Technology:

• Data warehouse applications use relational database technologies. This is because relational database technologies support quick queries against structured data.
• The Hadoop ecosystem is well-aligned to the data lake approach because of its agility. It can easily scale to large volumes and can handle any structure of data. 

Read more: Power BI or Tableau: Which is the better choice for your business

How both data lake and data warehouse can go hand in hand

Both data lake and data warehouse are the principal constituents of modern data architecture. A data lake usually serves as the starting point from where organization-wide data is onboarded. It is also the stage at which the data warehouse structures its data. An organization that incorporates both data lake and data warehouse will exhibit the traits of entrepreneurship and diligence, which means the organization will be both open-minded and scalable. 

The BI industry has tools that cater to highly unstructured data lakes that enable open-minded discovery. Also, there are tools that are designed to scale as a structured information delivery platform concurrently with your data warehouse. Though these tools oppose one another, they have very little in common. They are purpose-built according to the needs of an organization. So before choosing a tool you need to determine which one would be right for your needs and help your organization grow. Contact us now for more information!    

In this era of rapid digital transformation, new technologies have opened up opportunities and created challenges, fundamentally transforming customer experiences, operating models and the work environment.

While the scope, scale, and complexity of business technology has evolved at an exponential rate, sophisticated technology has also become more accessible to a wider audience. Such accessibility enables a thriving digital culture which can be a source of competitive advantages across all business functions – recruiting, training, sales, sourcing, manufacturing, logistics, marketing and more. While in the past, technology providers (like us at Fingent) predominantly worked with IT departments,  today we often work with functions like finance, sourcing, HR, project management and logistics, with minimal or no involvement of the customer’s IT team.

More importantly, access to robust technology is also no longer exclusive to large enterprises. Commoditization, outsourcing, and good connectivity have driven down costs, making technology accessible to businesses of all sizes, across the globe.

By providing an attractive basis for innovation, improving cost efficiency and differentiation, the synergy between technology and business processes is no longer optional, but a must-have.

Studies reveal that 55% of startups have already adopted a digital business strategy compared to 38% of traditional enterprises.

While the specific technologies that can be leveraged for business growth, will vary widely across organizations, there are a few common themes that business leaders can consider.

The Cloud democratizes Information Technology

Cloud computing is really the internet as we use it today. Dropbox, One Drive, Facebook, INFINCE, AirBnB, Twitter, Uber…. Are all in the cloud. It is really an umbrella term that covers a variety of on-demand computing and storage services as IaaS (Infrastructure as a Service), PaaS (Platform as a Service) or SaaS (Software as a Service).

Related Reading: Choose the right Cloud service model for your Business

Cloud technologies help discard or avoid the need for physical IT infrastructure, and on-premise support structures for computing capabilities, by virtualizing these across server farms or data centers. Using cloud-based services providers, businesses can leverage IT assets as programmable resources, which are global and scalable on demand. This allows a business to access or lease computing resources and storage power far greater than what it may have been able to access on local infrastructure, while still being able to scale up or down in a cost-efficient manner.

Consider V Locker, an Australian firm providing automated locker solutions for freight deliveries. V Locker manages lockers for B2B customers across the globe from Australia, using IaaS (Infrastructure as a Service) and PaaS (Platform as a Service).

On the cloud, multi-tenancy enables effective resource utilization, reducing costs to make the cloud a cost-efficient option for most organizations. For e.g. SaaS (Software as a Service) Property Management service Simple Rent uses multi-tenancy to provide a low-cost, high quality offering to the commercial and residential rental business.

While the Enterprise IT spend on the cloud is relatively small, it is the fastest growing segment, slowly replacing on-premise systems. At one end of the spectrum, Oracle and Microsoft are slowly shifting legacy products to the cloud, pushing many large enterprises to follow suite. At the other end, solutions like Infince have taken enterprise cloud a step ahead by blending SaaS and IaaS for small/medium sized businesses, providing a cost-efficient, secure cloud-based alternative to expensive alternatives. The cloud makes robust enterprise technology accessible globally to businesses of all sizes without the need to invest in expensive infrastructure or large teams.

Data – Big, Small and everything in between

With the relentless digitization of business and society, we have access to extraordinarily large amounts of data. Transactional data (from digitized business processes via ERP, CRM, HRMS, POS, and similar systems), Social data (Facebook, LinkedIn, YouTube, Twitter and the like) and Operational data (from connected devices and IoT systems) can be leveraged to provide better customer experiences and improve operational efficiency. The key is not just to gather data, but to leverage it with analysis and insight. From an organizational perspective, this can require experts from multiple disciplines to work together to peel back multiple layers of data and insight.

Related Reading: Find out how Big Data is changing the Healthcare sector.

Success depends not on the indiscriminate application of technology to data, but on a coherent approach, of identifying critical data that matter, and using the right technology to generate relevant and actionable insights, delivered to key stakeholders in the value chain, in real time.

In the realm of marketing, successful big data analytics manifest as tracking everything a customer or prospect does and generating real-time alerts to the marketer or a front line executive dealing with the customer. For instance, if the customer walks into a store, the automated analytic solution alerts the sales executive immediately, and everything related to the customer, including their preferences, purchase history, and more, surfaces to the executive’s tablet. Likewise, if a prospects click on an ad or downloads an app, the marketer gets an alert immediately, enabling them to engage the customer proactively, to close the deal or move the prospect up the lifecycle.

From Digitization to Digitalization to Digital Transformation

Digitization is the conversion of analog physical objects into digital goods. Paper to PDF or Doc, or physical cash digitized to mobile payments, physical signatures to electronic signatures – these are all digital manifestations of non-digital objects. Digital goods have low marginal costs, are non rival, and can easily be bundled with other digital or non-digital products. Consider online user manuals, learning management systems. Usually the first step in an organization’s technology journey, Digitization sets the foundation to enable Digitalization and Digital Transformation.

“Digitization and digitalization are two conceptual terms that are closely associated and often used interchangeably in a broad range of literature. There is analytical value in explicitly making a clear distinction between these two terms.” – Scott Brennen and Daniel Kreiss

Digitalization is about leveraging technology to create, enable or transform a business process- usually leading to one or more of- the discovery or new opportunities, reduced risks or efficiency gains. For example, field service management solutions like ReachOutSuite help deploy digital forms to field technicians across various locations.  It reduces the risks of revenue loss due to errors, inefficient scheduling and underprepared staff. This service also increases efficiency by maximizing staff utilization and getting more jobs done pre-staff. It further enables the identification of new opportunities by enabling techs and backend admins to understand customer experience better. Digitalization of business processes is par for the course these days with a plethora of packaged and custom built software available for enterprise planning, managing business finances, training, projects, customer management, and human resources.

Find how ReachOutSuite can make a work order manager’s life simpler.

• CRM systems coordinate business processes that are key to generating leads, converting them into prospects, and, subsequently, into regular customers. Additionally, CRM software solutions supply business managers with data processing and analytic tools to help refine marketing strategies, improve customer service and track overall organizational performance. Through centralization of business data, CRM software tools streamline the decision-making process and automate repetitive tasks.
• Project management software eliminates laborious paperwork and tedious planning processes. With the right tools, businesses can control projects costs and improve the efficiency of related operations. Technology automates most project management processes to make it affordable and practical for any type of business. The main benefits of implementing project management software include – Easier project planning, monitoring and tracking, Improved collaboration, Better organization, and future planning
• ERP systems boost productivity and promote business growth in two primary ways. First is automating business processes to improve accuracy and save time for all employees. Second, ERP systems unify data generated by the business and make it available to decision-makers and other managerial parties throughout the firm. It eliminates data sharing problems among departments and makes the information accessible to everyone.

Digital transformation is about leveraging digitization and digitalization to transform a business unit’s or an organization’s approach to business. This can involve one or more of – new business models, overhauling customer experience, radically different manner of service or product delivery. The transformation is driven by the business, and not by the IT team. For instance,  consider Replika, which connects brick and mortar sales to the digital realm, transforming the way sales is managed for retail. Emerging technologies will create new business models that may be hard to understand or foresee today. For instance, digital securities based on blockchain based technologies can unbundle ownership of analog assets like property or gems, while making it possible to bundle diverse asset classes to create new portfolios for investment. Such digitization of previously illiquid assets creates new customers, new strategies and new business models that may not be possible to fully comprehend today.

Related Reading: Find out how INFINCE is the ultimate digital transformation for small business of today.

In conclusion

Technology is a disruptive force.  In the current ever-changing and multifaceted business environment, technology can not only help improve your businesses’ agility but can also provide cost-effective means to innovate your products and services, improving customer experience.  The key is to adopt the right tools and partners, while actively planning the change and deployment.