The top five data challenges for enterprises today
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
To conduct analyses effectively, companies must transform their data. Only by doing so can they operate in a more data-driven and results-oriented manner. Many organizations have already taken the first steps by implementing initial strategies and measures, often encountering similar challenges along the way. The five most commonly cited challenges are:
1. Gaining meaningful insights from data
veryone 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.
2. Bringing together data from different 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.
3. Store data consistently and ensure good data 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.
4. 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.
5. Security and Governance
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.
Data management, storage, processing and analysis have evolved in recent years. This leaves one major challenge: too much data and too few insights that can be gained from that data.
Conclusion
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.
Discover our data and AI solutions for your business.
