Previously the focus was on how to store Data, rather than finding value from it. If we only wanted to process data, big data strategies would be just fine. Today we want more, and actionable insights are the pot of gold of data processing and analytics. We need to find the value in the tidal wave of data.
In the past five years we have started to move away from creating huge electronic storage areas for data, and began to use it in real-time. Most enterprises’ data today is still stored in disparate systems waiting to be processed. Value comes from taking the right data from multiple sources and using it effectively and efficiently to solve business problems and optimise processes.
Enterprises now understand the need to transform its data to perform analytics efficiently, becoming more data driven, and outcome-focused. Many of them will have early strategies and operations in place. When speaking to businesses about their data, they express similar challenges from their early stages of their data journey.
Let’s look at the five main challenges when using data:
Can’t get meaningful insights from data: Everyone knows data is valuable and business have plenty of it, but few understand how it works and how to get the most from it. Some struggle with where to start.
Disparate data sources: The volume of data is increasing with more types of data coming from different places, and there is a big challenge to incorporate them into an analytical platform. If this is overlooked, it will create gaps and lead to wrong measures and insights. Selecting which technology will be best suited to them without the introduction of new problems and potential risks can be challenging.
Data storage and quality: As the volumes of data increase, storage is becoming a real challenge. There is risk in collecting data poorly and throwing storage at the problem. The real issue arises when trying to combine unstructured and inconsistent data from diverse sources, it can encounter errors. Missing data, inconsistent data, logical conflicts, and duplicate data all result in data quality challenges.
Lack of expertise: Many enterprises have the same problem, not enough qualified resources (people, money, technology, time) to do the work they need with data.
Security and Governance of data: The lack of data governance and investment as enterprises grow can be a big downfall, especially for privacy and security. The use of disparate data can eventually lead to a high risk of exposure of the data, making it vulnerable if a consistent disciplined approach is not taken.
Management, storage, processing and analytics have evolved over the past few years to create the world of big data. Leaving a bigger challenge - too much data and not enough insights derived from that data.
It's not the amount of data collected that matters, but how useful the data is. As we move into data-based decision making, we need to ensure the process of collecting, storing, and processing to refine and clean the data we need is done effectively.