Business intelligence (BI) combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations to make more data-driven decisions. In practice, you know you've got modern business intelligence when you have a comprehensive view of your organization's data and use that data to drive change, eliminate inefficiencies, and quickly adapt to market or supply changes.
The next-gen of Business Intelligence is extensible, supports white labeling, and has open APIs for customization. Business users today rely on fully automated Business Intelligence systems without even requiring large data science teams.
Over the decades Business Intelligence has undergone a few iterations. The BI you see today is a far cry from its avatar of about 50 years ago.
In those days, was within the exclusive scope of a motley few in the top echelons of management. In addition, traditional platforms came with hefty price tags and were also time-intensive applications.
Then, over time, because of advancements in technology and computing, Business Intelligence platforms metamorphosed into self-service analytics. It was open to data analysts and classified by data visualization, preparation and discovery.
We started with “Gen One” BI - on-premises data and heavy-IT driven reporting projects - which was burdensome for both IT teams and end users. While cumbersome and requiring heavy investments, a few enterprises overcame those hurdles and reaped big benefits, lighting the spark for analytics.
Next, we entered the era of data democratization and self-service, or “Gen Two” BI, which focused on making data more consumable, easy to use and accessible by putting the power of analytics tools into the hands of business users. Although more organizations adopted Gen Two BI and gave out more licenses than ever, this generation's fundamental flaw was its focus on dashboards and analytics itself. We asked business users to also become analysts, to step out of their daily workflows to build or find dashboards to leverage data. The result is that adoption of data analytics continues to be a struggle as data volume and complexity grows, with only 24% of firms claiming to have created a data-driven organization, an actual decline from prior years!
It is time for a third generation of BI. Like the iPhone, which built on two generations of cell phones and smartphones and finally put mobile technology into the hands of every consumer, this new generation of BI will take the best of the two previous generations and finally empower every worker to make smart, data- driven decisions.
This will be “Gen Three” BI, and we call it: the Age of Action.
What does it look like?
Business intelligence is built on an old data culture that relies on technical experts. In the early days of reporting, those experts were called IT. As technology evolved and tools became easier, the progression of BI moved to reports and dashboards delivered by new experts—analysts. This made analytics more accessible, but still didn't make self-service data insights a reality across the business. Here's why: Instead of using technology to put data in front of people where they already are working, we continue to ask people to leave their business apps and turn to dedicated tools or dashboards for answers.
This process is disruptive and inefficient, and often causes users to write it off completely. Dashboards don't have built-in analytics processes; they share information but do not provide recommended courses of action at the right moment or in a decision maker's workflow. Business professionals want exactly that: They want a final answer and recommendations on what to do next. They would rather have data and actionable insights come in easily digestible bites versus needing to dig for answers in dashboards and reports. And the truth is they are digging; dashboards are often too broad to address multiple questions, too difficult to customize, and frankly, have too many insights.
Next Generation Business Intelligence can be analysed with respect to different
analytics such as-
• Enterprise BI
• Embedded BI
• Cloud BI
• Mobile BI
Analyze. Collaborate. Innovate.
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Data-Driven Decision Making
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Experience agile reporting and analysis with our intuitive data visualization and interactive dashboards. Give the power of self-service analytics to your employees and enable data-driven decision making across the hierarchy. Ask deeper questions, identify trends, recognize outliers, and optimize processes. Drag and drop, drill-down and up, slice, and dice your data to extract performance inducing insights.
Data Science and Machine Learning
Generate machine learning driven insights and make forward looking strategies. Uncover new business opportunities, foresee market trends and patterns, minimize risks and errors, and enhance your bottom-line. Empower your people with what-if analysis and predict potential strategy outcomes before implementation.
Access business insights anytime anywhere. Execute complete 360-degree analysis of your business right on your mobile. Check your business status and get real-time insights to make quick decisions. Create, discover, collaborate, be productive, and be on top of your business even on the go.
Transform the way your customers use your software application with business analytics. Give them an edge of AI and machine learning driven insights and ETL right inside your application. Generate new revenue opportunities by expanding your app offerings to embedded real-time reporting and interactive dashboards.
Add the power of comprehensive business intelligence to your application and enable your users to visually analyze your application data with self-serve embedded analytics.
A complete cloud BI solution that enables you to get deeper insights from your data, whenever you want.
In data-driven organizations, self-service analytics or business intelligence (BI) systems enable nontechnical users — such as executives or marketing staff — to access data, perform queries, and generate reports so they can make effective business decisions.
In organizations with traditional analytics and BI systems, when a nontechnical user wants to run an analysis or generate a report, they submit a request to a data professional, who examines the request, locates relevant datasets, constructs and runs a query, validates the results, and creates a visual report. The process might take as little as a couple of days or as long as several weeks.
Self-service analysis tools, on the other hand, automate much of this process and allow nontechnical users to explore data and share visualizations, while maintaining security protocols to protect sensitive information
The definition of mobile BI refers to the access and use of information via mobile devices. With the increasing use of mobile devices for business - not only in management positions - mobile BI is able to bring business intelligence and analytics closer to the user when done properly. Whether during a train journey, in the airport departure lounge or during a meeting break, information can be consumed almost anywhere and anytime with mobile BI.
Mobile BI - driven by the success of mobile devices - was considered by many as a big wave in BI and analytics a few years ago. Nowadays, there is a level of disillusion in the market and users attach much less importance to this trend.
Survey data from BARC's BI Trend Monitor 2018 shows that market penetration is growing relatively slowly: in 2017, 28 percent of BI users stated that mobile BI is already in use in their company (up from 23 percent in 2016, 21 percent in 2015, 18 percent in 2014, and 16 percent in 2013 and 2012).
Previous generations of BI gave us technology like data visualization to make data more understandable, the cloud to enable access from anywhere or any device, and extensible frameworks so we can embed data into other applications. Today, when we combine that technology with AI, we can extract data insights and embed them into our CRM, workplace collaboration apps, custom business apps, etc. Instead of asking users to pause their jobs and dig through dashboards for answers, we can put digestible insights and expert knowledge in the apps they are already using.
In this type of world, technology fades into the background. Business workers are in and out of their apps and barely aware that they're using analytics at all. They're simply getting the answers they need, making smarter decisions and moving on to the next task. Taking action based on data becomes seamless, automatic and instinctive for all.
In today's customer-driven times we live in, where business is defined mostly by a customer's wants and need, the success of a business is linked to how fast it responds to a client's demand. Real-time interactions across channels require real-time solutions, and that's why next-gen Business Intelligence deployments are required. Most businesses now prefer real-time or near real-time insights to make fast and accurate decisions. The next-gen of Business Intelligence is extensible, supports white labeling, and has open APIs for customization.
Next-gen BI applications are disrupting established business-analytics processes, just as data warehouses, for example, once disrupted disconnected data silos. Data warehouses today are equipped to draw inputs from embedded analytics, allowing analytics on “live” data in a business process. Eventually, the aim is to develop BI platforms that are available to all and require no data science knowledge.