Category: Analytics

If your company is dealing with Big Data, Data analytics and visualization is a must-have tool to understand and gain insights effectively. There are several tools available in this sector that promise to convert raw data and produce it as solutions that are easily understandable. These solutions can be in the form of charts, videos, infographics or even highly engaging and intuitive presentation that uses Virtual Reality and Augmented Reality (VR & AR). Here are the top 7 data analytics and visualization tools that you can hire to get the most out of your data.  

1. Tableau

Tableau is one of the most widely used data visualization software due to its user-friendly, intuitive design, and excellent official supporting documentation. This data analytics and visualization tool are available in five different versions – Desktop, Server, Online, Mobile, and free-to-use Tableau Public. It has the capacity to produce interactive visualizations that are compatible with huge and very fast-changing data sets used in Big Data operations involving artificial intelligence and machine learning applications. It is designed to work great with almost all advanced database solutions such as Hadoop, Amazon AWS, My SQL, SAP, and Teradata.

2. Sisense

This tool is smartly designed to provide a full stack analytics platform with ease of use. The interface offers users a chance to simply drag and drop charts and more complex graphics for achieving interactive visualizations without much of a hassle. The tool has the capability to gather data from multiple sources and query them instantaneously along with an option to share the dashboard across organizations. Sisense is a powerful tool for people who are experienced in this field and can be a little overwhelming for newcomers.

3. Plotly

Plotly is a web-based data visualization platform that lets the user create simple to complex charts directly inside their web browser. It offers a clean, intuitive and fully-featured interface for free, but its commercial package provides more user-friendliness and support. Its ability to enable more complex and sophisticated visualization is a direct result of integration with analytics related programming languages such as R, Python, and Matlab.

4. FusionCharts

This Javascript-based data analytics and visualization tool has the ability to produce 90 different chart types and integrates a large number of platforms and frameworks to offer optimum flexibility. It is one of the leaders in the paid-for market simply because it offers many live templates where users can simply plug in their data instead of starting one from scratch.

5. Google Charts

Google is at the forefront of technology and it stays the course by providing an entire set of data visualization tools that support various data formats including geolocation and offers a variety of outputs. But keep in mind that Google Charts are for people who have worked with data before and can code in JavaScript to make the best use of this amazing tool.

6. QlikView

QlikView tool is a major vendor that competes for supremacy with Tableau since it also provides a wide feature range and high customization setup. It does take time to get used to it, but can be very powerful when used to its full potential. There are numerous third-party resources available online for this clean and clutter-free tool. It additionally offers powerful business intelligence, analytics, and enterprise reporting.

7. Datawrapper

Datawrapper is the perfect tool for media organizations that are looking for creating simple charts to present statistics. It’s simple and clean interface allows easy CSV data upload, to create simple charts and maps that can be quickly embedded into reports.

Data analytics and visualization tools are abundantly available and seeking the best one for your organization can be difficult. It is better to seek the expertise of consultants such as our organization to get the most out of your Big Data.

The year 2017 has been eventful for digital transformation, with technologies such as Big Data coming of age. Digital transformation promises to be more disruptive in the coming years, as evident from the following predictions.

1. IoT Becoming Mainstream

The much-hyped IoT will walk its talk fully in the future. About 8.4 million “things” are already part of the IoT ecosystem, a 30% increase from last year’s levels.  Enterprises will start using IoT to deliver better products, services, and insights. IoT will permeate to the masses, and become mainstream and commonplace.

Among specific sectors, IoT will revolutionize analytics in a big way, driving new smart solutions. A few possibilities include hyper-efficient fleet operations, intelligent traffic signals, and more. Tech giants such as Microsoft, IBM, SAS, and SAP are all heavily investing in IoT Analytics, offering a portent of the things to come.

2. Edge Computing Offers Effective Real-Time Processing Solution

The cloud and Big Data analytics analyze tons of data seamlessly. However, the sheer volume and velocity of data create time lag and some inefficiency. For instance, the cloud, for all its advantages, is not viable for IoT powered smart drones, autonomous vehicles, and other AI-powered smart devices. These devices need real-time and instantaneous data processing. Sending data from these devices “all the way” to the cloud is an inefficient and impractical method of processing data.

Edge computing, which performs data processing at the edge of the network, near the source of the data, promises effective solutions to the shortcomings or limitations of the cloud and big data. Edge computing, for instance, allows IoT powered devices to connect and communicate instantly and seamlessly.

Industry biggies, such as HPE and CISCO have already rolled out hardware and software to actualize Edge computing. IDC predicts 40% of all computing to happen at the edge in the next couple of years.

3. Enter the 5G Network

4G or fourth-generation wireless, synonymous with LTE (Long Term Evolution) technology, went mainstream in 2017. 5G of fifth-generation wireless will go gain ground in coming years. The need for hyper-connectivity and IoT propels the need for 5G.

The Next Generation Mobile Networks (NGMN) Alliance defines 5G as “an end-to-end ecosystem to enable a fully mobile and connected society.” 5G is not an incremental upgrade over 4G or LTE.

While the 4G focus is on raw bandwidth, 5G focuses on pervasive connectivity and super-dense network, enabling even faster and resilient access to the Internet even from the remotest caves or desolate hills. Unlike the hitherto monolith networks entities such as 2G, 3G or 4G, 5G co-opts a combination of technologies, including 2G, 3G, LTE/4G, LTE-A, Wi-Fi, and more. While emerging technologies and solutions, such as IoT, connected wearables, augmented reality and immersive gaming places a great strain on incumbent networks, 5G will run these technologies seamlessly.

Industry majors such as Sony and Samsung are investing in Gigabit LTE, the stepping stone between the incumbent LTE and 5G. The already well-entrenched Qualcomm Snapdragon technology powers Gigabit LTE. However, 5G will become a household technology only after a few years.

4. Blockchain finds Its Way

Blockchain, the secure transaction ledger system distributed across a network of computers, rather than under any single entity, will finally become mainstream by 2018. The financial industry has already started to embrace blockchain in a big way. The healthcare, entertainment, and hospitality sectors are on the verge of embracing it in a big way. Dubai is rapidly moving toward becoming the world’s first-ever blockchain-powered government by 2020.

5. Artificial Intelligence Becomes Mainstream

The market size for AI is set to double up from $2420 million in 2017 to $4066 million by 2018.

Solutions powered by Artificial Intelligence are already popular. Artificial Intelligence already powers many popular solutions, such as Alexa, Siri, Salesforce Einstein CRM, IBM Watson, SAP Leonardo, Netflix, Amazon AI, Cortana Intelligence Suite, and various customer service chatbots.

Companies will continue to roll out cutting-edge solutions based on Artificial Intelligence, especially to implement smarter and cheaper automation. The scope of artificial intelligence would expand to encompass everything from emails and content generation and from industrial manufacturing to smart grids. In fact, major companies will embed Artificial Intelligence into their core operations.  Fueling the growth of Artificial Intelligence is the spread of open-source solutions. Artificial Intelligence cannot easily be integrated into closed systems.

With more investments being made betting on Artificial Intelligence, Virtual Reality is losing the race significantly because Artificial Intelligence offers whatever Virtual Reality offers, in a much cheaper and better way. For instance, companies can apply 3-D visualization to train, pitch, and envision new products in a much better way than what Virtual Reality offers.

6. Anything as a Service

XaaS or “Anything as a Service” is now within the realms of possibility. The cloud-based services market now encompasses software, infrastructure, and everything else. The latest to enter the “as a service” market is a framework!  Framework-as-a-Service (FaaS), which falls between SaaS and PaaS is a customizable cloud-based platform. Users may indulge in rapid prototyping, visualization, and other fast fail methods to discover whether a concept or strategy will work or not. Companies get to know the result of their initiative, without having to spend time and effort, doing it the hard way.

Side-by-side with FaaS, workplace-as-a-service (WaaS) and unified-communications-as-a-service (UaaS) will also become mainstream in the future. Remote workplaces, powered by WaaS, will rise in a big way in the future.

7. Low Code Platforms to Soar in Popularity

Low code development platforms (LCDP) will net a total revenue of $6.1 billion by 2018, and over $10 billion by 2019. These figures are impressive when compared with the total revenue in 2015 which was just $1.7 billion.

LCDPs allow creating apps through a configuration of functions, and intuitive drag-and-drop options, rather than by hand coding. The obvious advantage is ease of development and accelerated delivery of business applications. Acute shortage of talented programmers fuels the growth of LCDPs.

Some popular LCDPs, as of now, are Appian, Mendix, Google App maker, and Zoho Creator. The growing popularity of such low-code development platforms will spur ‘citizen development’.

Technology is always fluid. Companies who embrace the latest technology head-on to deliver better solutions for its customers stand to reap dividends. Companies who do not change will surely be swept away into obsolescence.

What is Cognitive Computing?

Cognitive computing is the simulation of human thought processes in a computerized model, or an attempt to make computers mimic the way human brains work. Cognitive computing platforms help create automated IT systems capable of solving problems on its own, without human assistance.

Cognitive computing epitomizes a shift from programmed-systems to systems that learn and understand. Apps using cognitive computing technology feature self-learning algorithms. It makes use of techniques such as data mining, natural language processing, and pattern recognition, to acquire knowledge from the data it mines or is fed into it. The system looks for patterns and refines itself as it encounters more and more new data.

Impact of Cognitive Computing

Cognitive Computing is a disruptive technology, with major transformational impact. It has the potential to take enterprises much further than what traditional business intelligence and analytics solutions take them, manifested in several real-life use cases already widespread now.

The range of insights offered by cognitive applications extends far beyond what a traditional analytical tool provides. The process of continuous improvement and refinement makes cognitive application systems capable of anticipating new problems and modeling possible real-life solutions.The tips and recommendations get better with time, as more and more data enters its range.

Cognitive computing is already in widespread use in several artificial intelligence applications, such as robotics, virtual reality solutions, and more. Among the various sectors where the adoption is already widespread, a key area where results are already obvious is in healthcare, fitness, marketing, and sports technology.

“Coach Watson” to Set the Stage on Fire

Cognitive computing is all set to unleash big disruptions in sports technology, and change the way athletes and other stakeholders approach major sports tournaments. Leading the disruption is IBM Watson.

The versatile IBM Watson is donning a new hat, of coaching elite runners in America’s top running clubs. While there are several apps already leveraging big data to offer valuable insights and optimize an athlete’s performance, “Coach Watson” goes beyond. As it is with any typical cognitive computing application, Coach Watson pulls in data from a wide gamut of sources, analyze such information in real-time, identify trends, and deliver actionable tips to athletes, in natural language.

The following are some sources from which IBM Watson gets inputs, on the basis of which it conducts its analytics:

• Watson collects physiological and biomarker data from wearables worn by athletes.
• The system takes in the tons of unstructured data which conventional analytical tools usually fails to factor. Examples include travel schedule, the attritional effect of a long season, work and family stress, body recovery time, and more, all of which are seemingly not related but can play a critical role in an athlete’s performance
• The system pulls in latest research findings from medical journals, health and fitness history of top performers in the particular environment, and more.
• IBM has co-opted Orreco, a working partner with Oregon Track Club for six years running. Orreco provides insights on physiological test data and biomarker data and data on nutrition and sleep.
• IBM has tied up with Triax Technologies, 113 Industries, and Spare5 to enlarge the Watson ecosystem, and further deepen insights.

The insights generated by cognitive computing apps such as Watson facilitate drawing up highly customized training programs and optimizing schedules. For instance, a Cognitive computing powered app draws in data from the wearable devices, compares it to optimal body parameters, and indicate how much harder or longer to carry on with a workout, whether to lower the intensity of the workout, take a few days off to recover on encountering fatigue, how to optimize sleep schedules around travel, and several other rich insights. Such insights, hitherto not easily discernable with any authority, ensure each athlete remains in a zone of peak performance on the day of a competition and could make all the difference between a medal and an “also-ran.”

At another pane, Coach Watson works to preempt injuries, which can severely disrupt or even end an athlete’s career. For instance, continuous monitoring of biomarker data could make evident iron deficiency in the blood, offering early warning signs of an upcoming injury, weeks in advance.

Watson Powers Many Actionable Apps

Cognitive applications are in the throes of revolutionizing sports technology. Many Watson powered cognitive solutions have already hit the market, to empower coaches, trainers, players, managers, and other stakeholders in a big way.

The Triax Smart Impact Monitor (SIM), a wearable sensor embedded in headbands or skullcaps track the force and frequency of head impacts, offering proactive assistance to mitigate the situation. The device factors in diverse data sources to offer sound advice on the best course of action.

The cognitive app powering Spare 5’s Watson Golf Pro is akin to a highly resourceful personal caddy. The app accumulates a corpus of knowledge on mechanics and drills, gathered from contracted golf professionals. Amateur golfers may consult the app in natural language, and gain crucial insights related to the course, such as wind velocity, the best shots to play, and more. Seeing a golfer’s swing, the app provides feedback for improving that swing.

Cognitive Systems Lend Objectivity to Player Selection and Management

There is a big element of subjectivity in sports selection and management. Cognitive apps can offer greater insights which allow selectors, coaches, managers and other decision-makers to make informed decisions, and support their gut instincts with hard data.

For instance, cognitive computing apps can read and interpret all the press conferences made by players over the years, offering insights to selectors and managers on the mindset of the player, as in whether the player is becoming more positive, more optimistic, and other innate characteristics traits. Likewise, coaches get detailed and in-depth information about the player’s physical and mental faculties, and how they react to specific situations, allowing them to take proactive actions to manage the player well.

Cognitive apps have the capability to analyze unstructured data, meaning it can make an in-depth analysis of videos and footage of the athlete’s performance, and give real actionable recommendations. This is a big improvement from coaches and training staff watching videos, and generating pointers, manually, based on their gut instincts, and with a high degree of subjectivity and assumptions.

Cognitive Applications Enhance Fan Experience

Cognitive applications work wonders not only for athletes and the business side of sports but also for fans. A good case in point is 113 Industries “Pi” service, which has been put to good use by Pittsburgh Penguins, the famous hockey team, to analyze the huge volumes of fan-based data. The club uses such insights to develop specialized offers and services for fans, relating to concessional merchandise, pre and post game entertainment at the CONSOL Energy Center, and more.

Cognitive apps could likewise guide fans proactively, such as making explicit the train to catch to reach the stadium on time, factoring in traffic, fares, preferences, and other innate traits which the fan had to consider manually before. Cognitive apps would also guide the fan to the best parking space, without having to drive around in circles. Once inside, a cognitive app could guide the fan to the toilet with the least wait time. All these are possible in natural language.

The application of cognitive computing to enhance sports technology can deliver rich rewards across the board for all stakeholders, players, tournament organizers, coaches, spectators, fans, television broadcasters, and everyone else. However, as always, the devil lies in the details. Success depends on designing the apps right, with clarity on the objectives, and the development task done by competent and resourceful professionals who know what they are doing.

Cognitive computing in a broad sense refers to software mimicking the functioning of the human brain, to make better decisions.

Computers have caught on ever since its inception, owing to its ability to undertake lightning-fast calculations, much beyond the range of human capabilities. However, computing devices face a serious limitation in not being able to accomplish tasks humans take for granted, such as understanding the natural language or recognizing unique objects in an image. While artificial intelligence offers a start in this direction, cognitive computing represents the coming of age in this front.

Cognitive computing, in a sense, represents the third era of computing, with computers that could tabulate sums the in-thing in the 1900s, to programmable systems in the 1950s, and now cognitive systems.

Personal digital assistants such as Siri, already present in smartphones come close to cognitive computing but are not true cognitive systems. Such systems can only respond to a preset number of requests, whereas true cognitive applications give a thoughtful response, without being restrained to a preprogrammed response set.

How Do Cognitive Applications Work?

Cognitive computing aims to simulate human thought processes in a computerized model. To this end, cognitive applications use deep learning algorithms and neural networks and leverage the latest technological solutions such as data mining, natural language processing, and pattern recognition.

Cognitive applications draw on multiple sources of information, including structured and unstructured digital information, sensory inputs such as visual, gestural, auditory, information, sensor-provided information, and more. It then processes the gathered information by comparing it to the set of data it already knows. As such, the more data the system encounters, the more it learns, and the more accurate the system becomes, over time.

Cognitive computing applications integrate data analysis with adaptive page displays (AUI) to tailor content for the specific audience and specific situations.

IBM Watson, one of the earliest approaches to cognitive computing, offered a path-breaking combination of natural language processing, machine learning, and knowledge representation. Watson ingests questions or inputs in natural language mode, search its repository for information, develops and analyze hypotheses on its own, and generates answers, also in natural language mode. What made Watson successful was not just the combination of the multiple capabilities, but the seamless and powerful integration of such different capabilities in a way it influences each other.

Basic Characteristics of Cognitive Applications

Cognitive applications are a cut above ordinary applications, as evident from the following basic features or characteristics.

Adaptive: Cognitive applications are adaptive, capable of integrating information around its ecosystem, as it changes. These systems feed on dynamic data in real time, or near real-time, to master ambiguity and unpredictability. It adapts to the changing goals and requirements of the enterprise, which is common in today’s highly fluid business environment.

Interactive: Cognitive applications interact easily with users, and also with other processors, devices, and cloud services. Such seamless interactions allow users to make explicit their requirements comfortably, and the network ascertains the requirements automatically to some extent.

Iterative: Cognitive applications are iterative and stateful. These apps ask questions or find additional sources by itself when a problem statement is ambiguous or incomplete. It also remembers previous interactions, and pulls in suitable information relevant to the current context, from such corpus.

Contextual: Cognitive computing applications understand and identify contextual elements such as location, time, meaning, syntax, processes, regulations, user’s profile, and more, connected to its ecosystem. The apps act on the basis of such information, automatically.

Dark Data Compatibility: Cognitive computing systems have the capability to deal with “dark data.” Traditional business intelligence and analytics solutions are mostly unable to comprehend social media postings, electronic medical record notes, electronic fitness device readings, unstructured images, and the bulk of general data generated by users in normal day to day settings today. Cognitive Computing apps process such multi-structured and unstructured dark data, to pull out non-obvious insights and subject it to analytics. Combining such dark data with the readily available structured information such as customer records unearth patterns, relationships, and other contextual associations not discernable otherwise.

Cognitive Applications in Action: Use cases

While cognitive computing has been around for quite some time, it is only recently, with the advancements in technology giving it a boost, that it has come to the mainstream. Several practical use cases have already emerged.

Many businesses now use cognitive computing applications to connect with their customers and other stakeholders at a more personal level and offer highly relevant recommendations. Such apps modify the recommendations automatically as they understand more about the stakeholder, and as the situation unfolds. Furthermore, such apps pick up subtleties that traditional analytics would miss.

IBM Watson, one of the earliest manifestations of a cognitive computing platform, already finds widespread use in healthcare. The cognitive computing platform collates the entire gamut of knowledge around a medical condition, such as patient history, journal articles, best practices, diagnostic tools, and more. It then analyzes the information, and offer a recommendation in sync with the changing condition of the patient. It is virtually impossible for any human to possess such vast range of information, leave alone analyze it. Doctors may leverage such insights to adopt evidence-based treatment options considering all factors, including the individual patient’s presentation and history. This is a big upgrade from the present scenario where the doctor makes educated guesswork, based on grossly incomplete information, with the decision based on the doctor’s limited range of knowledge. The insights available through cognitive computing enable even fresher doctors to perform as effectively as experienced specialists.

Cognitive computing applications are also making its mark in a big way to improve consumer behavior analysis, facilitate personal shopping bots, in education, diagnostics, and other areas. A good real-life example is Hilton Hotel’s Connie, the first concierge robot, which helps visitors with regards to hotel information, local attractions, and more, with questions posed in natural language rather than computing language.

Cognitive computing delivers positive ROI. Enterprises have already succeeded in applying it to convert even traditional cost centers such as customer care to profit centers. For instance, a packaged goods company applying cognitive computing to resolve customer problems automatically, pre-empting the usual practice of customers raising a ticket, could achieve a 30% reduction in tickets. Considering the cost of each ticket was $24 to $160, the savings are substantial.

Enterprises adopting cognitive computing, however, need to develop purpose-built applications to address specific use cases relevant to their stakeholders. Success depends on not just technical competence, but the extent to which the cognitive computing technology is interwoven with the business or customer needs.

What is Business Intelligence?

Business Intelligence (BI) turns data into information. It is the sum of applications, technologies, and methods adopted to glean strategically relevant information for the business. The task involves collecting, integrating, analyzing, and applying historical, current and predictive information related to the business. The objective is to gain strategic insights, evaluate risk, and improve decision-making capabilities for top management, business owners, and other stakeholders.

Business intelligence information encompasses both internal data and external data related to the business. Internal data may include operational data, data from sales, financial data, and more. External information is fairly wide in scope and may include information from the external supply chain, social media channels and more. Another dimension to external information for business intelligence is information about competitors and other companies, for benchmarking, and to gain a competitive edge over such companies.

Many businesses now apply business intelligence to make informed decisions, rather than relying on hunches or unfounded assumptions. For instance, restaurant owners use business intelligence to decide the menu to be added and removed from a restaurant menu. Automobile manufacturers apply business intelligence to add new features to their car models. Tour operators use business intelligence to optimize their itinerary. The possibilities are endless. Businesses may use Business intelligence to modify their internal work processes as well.

Business Intelligence Vs. Analytics

One might mistake Business Intelligence as being another name for the much-hackneyed business analytics. While both business intelligence and analytics involve aggregating and analyzing data, the similarity ends there.

Business intelligence aggregates data to make it explicit what is happening to the business, whereas analytics aggregate data to delve into why it happens. For instance, business intelligence evaluates sales performance by extracting data from various sources and presenting it in the form of intuitive graphs. Analytics involves running statistical tests to make explicit whether sales performance correlates with advertisements and promotions undertaken.

While both Business Intelligence and Analytics involve analysis of data, the scope is markedly different. In a sense, Business Intelligence ends with the basic mathematical operations of addition, averages, and percentages to the dataset. Applying advanced algorithms complex formulas, statistical operations such as regression analysis, correlation analysis, and more, to answer deeper questions related to the dataset comes from the realm of analytics.

Technically, Business Intelligence encompasses ETL tools to access and transform data,  OLAP (online analytical processing) tools to handle complex queries, tools to manage metadata, development tools to generate various reports and dashboards, and others tools for publishing, scheduling and distribution capabilities. Analytics co-opt different methods of analyzing information, such as descriptive, predictive, regression, neural networks, and the tools to apply such methods.

What is a Business Intelligence Software?

Business intelligence software enables enterprises to undertake business intelligence tasks.

Any Business Intelligence system worth its byte involves the following activities, and software to undertake such activity.

1. Data warehousing, or bringing together all data, such as sales data, operational data, data from financial reports,  competitor sales data, social media talk, and more. ETL (extract-transform-load) tools usually facilitate the task of extracting data from several databases, systems, and applications, transforming it as required, and loading it into the target data warehouses, analytical applications, and other systems.
2. Data visualization, or presenting the information in a visually appealing way, for users to make sense of the data in a better way. There are different visualization types, such as charts, graphs, infographics, and more, and various enabling tools for each type. Each visualization type serves a specific purpose. For example, Pie charts are best suited to communicate relative performance, whereas bar charts depict trends in the best way. Some intuitive methods such as “slice and dice” are gaining popularity of late, for its powerful capabilities.
3. Management dashboards, or well-grouped set of visualizations containing most relevant information to any stakeholders, usually reserved for top management. OLAP tools, used to aggregate data and handle complex queries, are commonly associated with Business Intelligence.

Several types of tools enable data visualization and management dashboard capabilities.

• Spreadsheets such as MS-Excel are the most common method of presenting the Business Intelligence data. The ease of work nature and universal availability of spreadsheets are however marred by serious limitations, such as limited security, limited collaboration features, limited automation capabilities, and a high degree of inaccuracy and variability. Various other tools offer seamless and advanced automation, security, and audit capabilities, which MS-Excel lacks.
• Operational reports, often custom made for the enterprise, list data at a very detailed level, in a highly-structured and logical format.  
• Querying and analysis solutions allow business users and analysts to generate business queries and reports rapidly. The ad-hoc nature of these tools offers unbridled flexibility. These tools also come with intuitive graphical interfaces. A middleware layer which converts database conventions to normal business nomenclature, and dashboard drag-and-drop functionality spares end-users from the need for technical proficiency to use these tools.
• Dashboard management systems offer easy access to key information in intuitive and graphical formats.  It displays the required set of key performance indicators, metrics, and other relevant information in a single consolidated view, greatly improving the user’s decision-making capabilities.
• Data mining tools allow analyzing large data sets to discover patterns and build predictive models. data mining application use algorithms, statistics, advanced mathematical techniques, and sophisticated data search capabilities, to unearth insights otherwise not discoverable.
• Online analytical processing (OLAP) tools facilitate the rapid visualization and analysis of business metrics. Users may store and access data in multidimensional OLAP cubes comprising of measures, and text values. Queries from OLAP cubes are much more powerful than simple database tables and offer users advanced insights into past performance and various “what-if” scenarios. Users may use such cues to evaluate information from various dimensions and perspectives. In fact, OLAP operations constitute the fundamentals of most advanced functionality used by data visualization tools.

Top Business Intelligence Tools 2017

Until a few years ago, users had a limited choice of comprehensive Business Intelligence tools. The choice was limited to platforms offered by biggies, such as IBM, Oracle, SAP or Microsoft. The high cost of the software and the associated complexities made business intelligence out of the reach of most enterprises, except big corporations.

Read more: Business Intelligence in SAP: How It Helps You Become a Data-driven Organization

The advent of the cloud changed things, making business intelligence more democratic. Business no longer needed to build their own BI infrastructure in-house, as SaaS providers perform the resource-intensive tasks of data gathering, persisting and reporting, allowing businesses the ease, affordability, and convenience of the subscription model.

Some of the top ETL tools include Oracle Warehouse Builder, SAP Database Services, IBM Infosphere Information Services,  SAS Data Management., Elixir Repertoire for Data ETL, SQL Server Integration Services (SSIS), and more.

Some of the top OLAP tools in vogue include Yellowfin, Stratum, Birst, Profitbase BI, and the quintessential Microsoft SharePoint, among others.

Here is a rundown of some of the leading data visualization and dashboard software in 2017

• Dundas BI, one of the most popular browser-based business intelligence and data visualization platform, offers integrated dashboards, reporting tools, and data analytics capabilities.
• Domo, A cloud-based suite, makes it easy to integrate multiple data sources, including databases, spreadsheets, social media and also legacy cloud-based and on-premise solutions deployed in the enterprise.
• SiSense support business data with powerful visuals and tools, making it handy for users with little or no technical skills.
• BOARD, a centralized tool, hosts data and conducts multidimensional analyses. It boasts an impressive client list, which includes US Navy, Giorgio Armani, and Puma, among others.
• IntelliFront BI enables viewing multiple data sets from a single pane. It also distinguishes itself with strong automation features, real-time dashboards, and on-demand reporting.

Self Service Tools

The emerging trend of self-service business intelligence, made possible by the advancement of mobility, analytics, and other relevant technology, empower end-users with the ability to undertake business intelligence tasks by themselves, without necessarily having advanced technical skills.

As it is with several other sectors, Google has also entered the business intelligence space, and with time, is sure to give established incumbents a run for their money. It is now possible to build an intuitive BI dashboard powered by Google Data Studio reporting tool and Google BigQuery, a cloud-native data warehouse capable of analyzing petabytes of data in double quick time. As always, all these Google tools are open-source and free, giving developers the unbridled freedom to co-opt it in their systems, the way they want it.

Google’s new offering competes with Microsoft Power BI, another free, self-service cloud-based business intelligence offering from the Microsoft staple. Non-technical users may leverage this offering for aggregating, analyzing, visualizing and sharing data easily.

Businesses would do well to consider developing custom suites facilitating such self-service options, integrating ready-to-use Business Intelligence tools to the extent required. Partnering with Fingent top custom software development company would enable the business to roll out highly intuitive business intelligence apps and other solutions, empowering end-users to gain the required insights easy and fast, without having to wait for the IT team to do the job and spoon-feed them with reports.

In today’s digital age, a website is your business’ window to the world, irrespective of whether you are a brick and mortar establishment or an online entity. Similarly, the need for a dedicated website is not just limited to startups but is equally vital for established business. Even if your business is doing exceptionally well, investing in a well-designed and responsive website can help you reach out to a wider cross-section of the audience and increase your sales manifold.

Here is rundown on how custom website development services can turn the tide for your business:

Website Development and Its Advantages

Custom web development is one of the finest things you can do to guarantee the success of your business. In addition to improving your visibility, customized website development can help your business in the following ways:

1. Customized website development boosts your credibility and adds a certain brand authority to your business.
2. A professional-looking website can bring you big gains without an astronomical investment, thus, offering a higher return on investment (ROI) than traditional means of advertising.
3. It does not matter whether you are a newbie or a seasoned market player, there is bound to be competition in your realm of trade. A dedicated website can assist you in leveling playing field and even beating your competitors.
4. It boosts your advertising and marketing endeavors across different channels. Your social media marketing and email marketing campaigns are likely to resonate better with your audience if these lead them to a website.
5. Custom designed websites are cross-browser well-matched.
6. Custom developed websites also costs nearly same as of any template based solution. They may have little higher cost but offer numerous benefits and total control on each part and role of the website.

Specialized, clean, and well-organized online presence is essential for preserving your brand reputation. General designs can create a negative impression of the product or service quality, the reason being customers see you as no dissimilar from thousand other companies. The website represents your business in terms of superiority, quality, and individuality, so you need to have one developed skillfully.

Importance of Multiple-Channel Presence

At a time when smartphones and handheld gadgets rule the roost, the number of people accessing websites through a desktop computer is fast dwindling. Almost everyone likes to browse the internet on the go. Therefore, a website with a responsive design – that ensures your website is displayed correctly and uniformly across platforms, devices and screen sizes – is essential to stay relevant in the dynamic online landscape.

As per statistics, whopping 67 percent users are more likely to use an enlisted service or buy products on a website with a clean, responsive design. In contrast, 61 percent users are likely to exit a website with an unfriendly layout and design. The bottom line is – a potential user may bounce back from your website and move to that of your competitors if you don’t cater to a multiple-channel presence.

How Digital Marketing Can Help

Building a website from scratch is no big deal, given the user-friendly content management systems like WordPress. Even so, if you really want to make an impact with your web presence, it is advisable to use professional custom website development services because you don’t just need to build a carefully-strategized site but also work toward improving your search engine rankings to attract massive traffic. People will know that you exist, only when your website is visible in search engine results. A smart digital marketing campaign can help in bolstering your web presence to clock-in tangible gains.  

Enterprise software solutions are the backbone of an organization. Selecting one for your organization is a great responsibility as it requires detailed analysis at each and every step. The major challenge lies in understanding that every organization’s structure is different and cannot do with the same software that was used in your previous organization or is used at the competitor’s company. To make your task easy, here are 8 challenges to avoid before purchasing an Enterprise Software.

1. Change Management

One of the key understandings that should come with a new Enterprise Software is that there is a need for identifying the changes in the IT infrastructure beforehand and then implementing it. Know how much change is required and if it is possible to handle before purchasing the new software. Also, the employees need proper training for change management and to get them comfortable with the new system before it goes live on an organizational scale.

2. Physical Requirements

Before jumping into a purchase decision, figure out what is the current IT infrastructure landscape, if it fits well or requires changes. For a newer setup, choose the software wisely and then plan the landscape. But remember, the software you choose defines this landscape and should be thoroughly checked to avoid any unwanted changes in the future.

3. The load of migration

Data is the basis for every industry functioning in this day and age. Introducing newer software to the system means adding your current data to it for validation and future operations. Check the requirements of the new system you plan on incorporating and if the required is way too much. Remember data is imperative to the organization and correct migration will minimize errors.

4. What the vendor has to offer

Sometime Enterprise Software vendors are too enthusiastic to sell the product but lack a structure post-purchase. Figure out what are the key support promises that they offer and if they have a track of delivering them. A due diligence of the provider should not be skipped especially if the vendor is not a big company.

5. Readying the team

Internal challenges are one of the key points to consider for the purchase. You will have to understand the requirements of the enterprise software development company well along with the features required. Each department will offer a different list of features they need. The challenge is to create a project scope that encapsulates all these important points and uses it as a foundation to identify the right software.

6. Software scope

Considering any enterprise software, and its capabilities, it is quite hard to find one that has everything that a business needs. As organizations strive to meet individual customer demands, they rely on customization to align their strategic objectives with enterprise solutions. For instance, one of our leading real estate organizations wanted to add a document management function that enabled them to share documents easily with their field agents. However, this posed numerous regulatory constraints considering security and privacy. Our solutions experts realigned the software architecture and build processes to meet the client’s requirement.

7. Taking unwanted risks

The market has opened up for Enterprise Software like never before and every day a new solution surfaces that promises to offer the best of all worlds. New-age technology can be highly enticing but always look under the hood and see if it has been taken for a test drive. Check LinkedIn, job boards, or other forums to find out more about the seller and the software itself. Don’t elevate your risks by getting attracted to high-end technology or pricing.

8. No detailed analysis

One of the issues that hamper the procurement of the right enterprise software is the lack of understanding of your own system. Run the analysis several times and match it with what the software will provide. Keep in mind to not depend on what the next version update will feature, rely only on what the solution provides today.

9. Cutting the influences

Decision making in organizations sometimes doesn’t necessarily stay with the designated employees. It is directly or indirectly influenced by the owners or other people in important positions. No matter how well they know or what better deal is offered through a reference, ensure that the decision, in the end, comes keeping the organization in mind and not the influences.

10. Exploring all opportunities

Do not hesitate to look at third-party support alternatives as they might offer something similar or better than premium vendor support. Look at the maintenance rates that come with the software as it is something that your organization will incur on a regular basis.

Whether you are trying to set up a new system or replace an old one, an expert perspective can help reduce these risks and challenges.

Predictive Analytics has put a strong footing in the retail sector where companies are able to understand what their customers want, what products are in demand and are able to allocate their resources optimally. Going a step further, this very analytics is beneficial for targeted marketing to an individual level. Yes, individualization via predictive analytics is the biggest boon that companies can use to their advantages. Achieving the most granular of data today is possible and such multiple endpoint creations help in providing a customized solution for individuals instead of putting them under a group.

Why Individualization?

The huge wall between physical and online retail stores has fallen due to rapid digitization. With Omnichannel marketing strategies and by utilizing predictive analytics, a company can find what a particular customer requires. Their past history and behavior help in predicting what they might want or what more can they be offered. Individualization helps in narrowing down the possibilities of product attributes and can be presented as per the user’s data.

For example, if a person is constantly trying to look for a particular color or is worried about size, the retailer’s website can show them such options instantly. These can be even used for personalized emails or messages that can lure them to a nearest retail store.

Making the Most of Predictive Analytics

There is vast information available that can be used in different ways to implement predictive analysis. Retailers can tap into it and use it in the following few ways to make the most of it.

1. Improve Targeted Marketing

Individualization in a way invokes ‘a feeling of being special’ in the users. At the retailers’ end, it merely helps in offering these customers what they would purchase, understand their styles, trends, and sum up a personalized service. It can be even leveraged to understand what they want so that retailers can try to acquire or create a new segment of products for the same.

2. Keeping Them Interested

In today’s time and age, the pool of information for both retailers and users is so much that they can be lost in it. Customers are quick at switching from one retailer to another when they do not get what they are looking for or find things uninteresting. Using predictive analysis can definitely reel new customers but at the same time, it will keep them engaged. Retailers are able to sense if the customers are staying or leaving by simply surfing or checking the purchase history. Such actionable intelligence can push the retailer to bring back the customer by throwing incentives or providing offers that are based on their data analytics. Also, such data helps in keeping the current customers engaged by sending them timely offers and planning appropriate promotions.

3. Physical Store Value

User data analytics such as demographics, financial market conditions, and recent customer purchasing history can be used to understand where customers hang out the most. Although here it is more of a group analytic, such a data can help retailers to understand where to open a physical store location. Once the store is in place, individuals can be lured via various offers to a place where they prefer to visit.

4. Offering Better Customer Service

Predictive analytics helps the retailers to come up with a better marketing plan. Based on their individual data such as their interests, purchase history, likes, and dislikes, retailers can help them make informed decisions. Also, offering query resolution at individual levels makes the customers feel valued and can retain them in the long run. Such smart analysis will also help in using social media marketing to the fullest of potential.

Predictive Analytics for individualization can be used as the ace in the already swarming pool of marketing strategies. Get in touch with us to understand how predictive analytics tools can help in maximizing the potential of such huge data gathered and reach out to your customers better.

We are in a world of more data, and more complex data, than ever and big data is changing the way real estate professionals, buyers, sellers and even banks, think about transactions involving property. This infographic covers the impact of big data analytics in the real estate industry.

“Data is the oil of the 21st century.” The question is, how can we process and integrate it into current business processes to reap more profit? Every business today focus on taking their traditional business operation, applying digitalization, to produce something that customers are willing to pay for. Customers will make use of these changes if they see a gain in terms of monetary or time efficiency. So, digitalization, if applied to the real estate sector, brings tremendous returns in terms of efficiency, sales, ROI, customer satisfaction and more.

Digitalization might have made its mark in the Real Estate industry, but big data is the primary component of the current evolutionary process that real estate firms must harness. The infographic deals with the effect of big data, and the impact and application of big data processes in the real estate sector, offering tremendous potential which has not yet been properly exploited. The results are based on the findings of a survey comprising nearly 468 real estate companies from and around Europe.

Stay tuned, for the next infographic –  The Impact of Big Data Analytics in the Real Estate Industry, 2017