Why enterprises should start modernising their BI solution?

Why enterprises should start modernising their BI solution?
BI solutions

When we say or hear a term ‘BI solution’ we understand that it is a backbone for any business. However, with growing volume of data, traditional BI solutions are facing multiple challenges, like low performance or inability to ingest more data. Modernizing your BI solution with big data technologies can enhance its capabilities by multifold.

Out of many good reasons, I would like to elaborate on major 6 of them.

1. Today’s data is unmanageable

We are surrounded by variety of data, which is huge in volume and high in velocity. For example, emails, pictures, videos, audio and flowing streams of texts such as social media messages and blogs. All flowing at high velocity resulting into huge volumes. Traditional BI solutions were tuned to handle only high quality structured data. Mining information from data that is available in whatever format and size was not possible. All these variety of data have wealth of information that can benefit our businesses in understanding our customers, their choices and shopping behavior, their preferences and so on.

Lets take for example a small company that wants to know that which are the products that are most talked about? Now it is not possible to extract this information if you don’t have ways of capturing data from high velocity social media, where possibly people are discussing or tweeting about your product.

Today’s big data technologies make it possible to analyse this messy data and allow us to focus on what’s important!

2. Quick data access

Researches show that managers spend 80% of their time in searching for the information before making business decisions. Having right information in hand when needed is the most important aspect for any business to operate efficiently. Most BI solutions face performance crunch as you run analysis for on longer time-range you add more dimensions to your data.

For example, being a telecom provider, think of analysing data usage trends, session durations and attrition trends over last 5 years. This can involve GBs of data. It is just not possible to process and analyse this magnitude of data quickly and efficiently with traditional BI tools.

Big data technologies with distributed computing power can handle huge computations in the fraction of time. Thus, making your data quickly accessible.

3. Easier decision making

Many a times only key business people are well versed at using BI tools. Empowering your staff with self-service BI so they can access data on demand is an ideal business situation. However, with big data technologies, it is not a far fetched dream. Interactive dashboards based on not only limited set of data but terabytes of data, with meaningful visualisations, can empower your front-line staff by providing information in near real-time.

For example, by implementing big data solutions retailers need not to wait for locating which items to order next. However, smart shelves can directly inform the supplier about which items to ship next!

4. BI is not only about historic data

Previously BI was used to be of historic perspective. It was considered to help you understand ‘how your business was doing at some point of time’. However, modern BI solutions must be forward looking. Modernizing your BI solution with big data technologies can run predictive algorithms to identify opportunities that were never possible to capture.

For example, in the past BI tools took long time in identifying area-wise product sales for past quarter or so on. However, big data enabled BI tools can not only update entire sales staff with latest sales-figures but also predict cross-selling possibilities on the fly!

5. Survive in the competition

Uses of Big data is getting increasingly common and big players in the market have already started adopoting the Big Data technologies along with advanced machine learning and predictive analytics. The time is not so far when adoption of these all technologies will not be a luxury but it will be a necessity to survive in the market.

For example, consider big E-commerce players like Amazon, Ebay, Flipkart, Snapdeal and so on. They use advanced machine learning to analyse user behaviours and understand user’s interests. This helps them predict what user is more likely to buy and uses these informations to recommend products to the user. In recent report, Flipkart published that in last year, 40% of their revenue was generated from the recommendations.

This indicates that E-commerce market is so much mature in adoption of these technologies that as a start up if you don’t adopt to these technologies, it is extremely difficult to even survive.

6. Easy to get started

Big data enabled BI solutions can store and processes data that is horizontally scalable, meaning you can add more resources as you need. There is no need to occupy large resources right from beginning. You can start small, see the positive results and then scale. Big data technologies make it possible to store data in distributed manner on low-cost hardware.

For example, the best way to start into this, could be just creating a low-cost data lake for your discarded data. You can use some of the open-source analytics engines along with free visualization tools like Kibana to get insights out of the data that you were simply throwing away. And you can do this at very low cost.

Most BI solutions usually pull data from a data warehouse. And you rely on complex ETL processes to ingest data from different sources into your data warehouse. Big data technologies make it possible to quickly ingest new data sources thus enhancing BI solution’s capabilities. With growing population of social media, every aspect of our business is being datafied. For example, product choices, likely hood of sharing product review with friends. Influencing purchases based on friend’s review and so on. Thus, having BI capabilities for handing such variety of social data is a necessity.

Krishna Meet is a software scientist having core interest in analytical dashboards. Majority of her career span was into tech-writing and UX-design. However, she thrives by intersecting multiple skill-sets : SQL & NOSQL databases, business analysis, and UX design. She is a voracious reader and possesses Masters degree in Computer Science. Her interest in agile methodologies and user-centered design has landed her a techno-functional role at Brevitaz.


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