As the understanding that data insights can drive leadership and innovation gains traction, businesses have become increasingly data focused.
By Iniel Dreyer, MD of Data Management Professionals South Africa (DMP SA)
While data collection is nothing new, the reality is that many organisations have no idea what data they have, where it is located and how it can most effectively be leveraged. This scenario has become even more complex as businesses migrate to the cloud, creating more disparate data sources, duplication and data sprawl that can make gaining insights from data impossible.
The key to solving this challenge is data classification, as part of an overall data management strategy, in order to gain control over data and enable it to be leveraged to drive business value.
What are we doing with data
Without a data strategy, data is often collected indiscriminately, and with more and more data sources, including a growing number of sensors, connected devices and more, data volumes have increased exponentially. Lack of centralised control also means data is often duplicated multiple times.
This leads to rapidly accelerating data storage costs, which are increasingly problematic, particularly regarding cloud migrations, where costs are directly related to volumes. In addition, data cannot effectively be used for analysis, which means it cannot drive proactive decisions or add value.
Dark data like this is a burden, a cost, and a potential liability, rather than the strategic asset it could turn into. Businesses need to understand what data they have, where, why and what they can get out of it, otherwise they cannot leverage it for insight, and run the risk of falling foul of increasingly stringent laws around data.
Containing the sprawl
To use data as an asset, it is vital to proactively understand what data we need and what data we have, consolidate data sources, ensure that there are no duplicate copies of data, and have a strategy for extracting the data.
Critical to the ability to contain data sprawl and turn data into information, the business can utilisedata classification. Effectively categorising data helps businesses understand not only what they have but also what is important, how to prioritise data, and how to ensure that critical data types and locations are effectively identified and protected so that the risk around them is understood.
When data is classified correctly, data retention strategies are more effective, analytics is more impactful and data storage is less costly, because duplicates can be identified and weeded out and only data that is needed is kept. This also allows for far more effective insight and intelligence. Data classification should go hand in hand with data management and form a key part of the data strategy.
Working together
Data is not an IT problem, it is a business problem, because business needs to drive the decisions around what data must be used for, before IT can act as an enabler in delivering this. Business needs to understand what applications are generating what data and what information they require to drive decision-making.
Only then can IT and data management come together with business, to deliver the systems that will drive data classification in line with business requirements. Effective data management and classification relies on a central repository for accessing information, a single overview, and a single pane of glass for dealing with data to prevent sprawl.
All of this requires expert skills, which are typically in short supply and difficult to obtain in-house. Having the right data management partner can be invaluable, particularly as businesses migrate further into the cloud. Data costs can quickly spiral with a cloud-first approach, but with a classification strategy up front, these costs can be contained.
A data management specialist will help businesses to identify the pitfalls, ensure that a well-designed strategy is in place and that data can be consolidated and managed from a single pane of glass with as few tools as possible. This will help to mitigate risk, minimise cost and ensure data is compliant and available for the essential analytics that drive business insight, innovation and intelligent decision-making.