The term ‘big data’ does not seem to be adequate to accommodate all the data being generated at the pace at which digital transformation is growing. The terms gigantic data or massive data are the only things that come to mind after big data.
There are a few ingrained problems with advanced analytics services today and the term big data services masks them. The major problem of knowing how much data there is, seems to be solved by most of the companies. Storing data has become extremely easy with cloud storage. Along with this, heavy investments have been made by organizations in data warehouses as well.
Is analytics mature enough to make a difference?
Even today, not all companies have achieved maturity with their analytics that can be called transformational. Gartner conducted a global survey of 200 companies. It had a pre-set definition of what the highest level of maturity in analytics was – data and analytics being central to business strategy. Only 9% of the companies in this survey had reached that maturity level.
The big question that arises now is, why are organizations not able to derive any real value from the investments they have already made in predictive analytics services? To answer this question, there are three identifiable barriers:
Working with legacy IT and having to tackle massive amounts of data is a daunting task. With the patience of the users wearing thin and the top management asking for insights, data analytics becomes a not so easy job anymore. Data professionals speed up the results by just simplifying the data. Reports generated from just the extract of data are quicker and provide a better overview to the top management.
However, this does not give the actual meaning or understanding of the business of the market scenario. Anomalies may simply be averaged out and will not even be seen. You can’t blame the professional when a headline figure can’t be justified with granular facts as back up.
Data comes in from a multitude of disparate sources. These may be from the internet of things, customer transactions or even automation systems in marketing.
This leads to the formation of ‘data islands’ across legacy archival systems and databases. This further leads to inefficient data duplication and many repositories of data that are disconnected and have inconsistent structures.
In a study conducted in 2018, it was found that fragmentation was the cause for 50% of the medium to large organizations to not realize the entire value of the data they possessed. To gain a holistic view of their business despite fragmentation, the data islands are layered with business intelligence solutions and workarounds. These practices also include pasting of data on spreadsheets manually.
It isn’t a mystery that the workloads today are extremely data intensive and require supreme computational abilities. Inefficiencies and performance latency are experienced by the legacy systems in place as they are not built to handle the demands of today’s data.
Due to this, the legacy systems have gained the reputation of not being fit for the job. This becomes a critical case scenario especially when the obtaining the data that needs to get to the decision makers becomes a lengthy and problematic exercise.
What these barriers have resulted in are organizations having business intelligence and analytics solutions that are manually tasking, lack complete information and are eventually backward looking.
Setting things right!
The primary reason for these barriers to arise is because organizations today have not been able to implement a robust data management system successfully without struggle. We say that data is siloed, today’s data analytics approaches have become siloed as well.
It is pretty obvious that the first step to turn this around is to have a holistic approach which is organization wide in terms of architecture and data strategy. With that being said, it doesn’t mean that you will have to start all over again.
The already existing investments in data warehouses can be built upon. After all, the data warehouse is where all your historical data exists. These data warehouses will also contain the new processes of new data ingestion.
An in-memory database is one such technology that is leading the charge with helping organizations better their data infrastructure in terms of scale and performance.
Big Data processing done in memory will help obtain fast results as massive data sets surface easily. This makes the BI and analytics tools a lot more effective for the data professionals.
Businesses can bring together and analyze all the data thanks to memory efficiency – in turn making transition from BI to analytics easy by removing the latency and silos. Having an open integration framework has its own advantages. This ensures that organizations can upgrade their legacy infrastructure on a by need basis and will not have to overhaul the existing investments.
In-memory databases allow for BI reports to be delivered in seconds and not hours. This improves productivity and also makes the decision makers proper knowledge workers. This is the way to becoming a data-driven business and to drive transformation. Businesses will now be able to use the new and improved insights to combat all challenges that the market throws at them.
Analytics is the way to go!
Achieving this automated view of data should be the primary objective of all businesses. Forrester has segment called ‘insight-driven business’ – when businesses entirely master their data, they get classified in this segment.
It isn’t just getting into the segment; the results will speak for themselves after that. The companies that are insight-driven are currently growing at 30% year-on-year and are expected to earn $2 trillion by 2021.
Being an insight-driven business doesn’t mean you just collect data. You use the data in an insightful, profitable and meaningful way. That is when you gain the competitive edge and realize the actual power of data.