Data management is typically in reference to the structured data gathered from systems such as enterprise resource planning (ERP) tools or other databases. However, this type of data only comprises a small portion of the total data organizations create today, and most of it is ‘unstructured’. It is also estimated that by 2025, 80-90% of data will be unstructured.

Locating, storing, managing, and analyzing are only few of the challenges that organizations manage with unstructured data. While many enterprises are unaware of the origin of all their unstructured data, they need to adopt new approaches to control and optimize data management as it is crucial for increasing measurable corporate value.

Addressing three key challenges

Among an organization’s challenges with unstructured data, these three are central:

Lack of visibility

When organizations do not know the origins of their data (or what its status is), they cannot drive efficiency or gain value from information captured in an unstructured way. In effect, they are leaving money on the table.

Lack of accountability

Data protection regulations like GDPR are crucial to follow. Organizations risk serious consequences like fines of up to 10 million euros or 2% of a firm’s annual revenue from the preceding financial year if these guidelines are not followed through. Accountability and traceability are key but impossible to achieve if unstructured data is unorganized and unmanaged. Being able to track who did what and when is essential.

Lack of simplicity

Unstructured data management should be straightforward and without any human involvement. Data can be automatically integrated with AI and machine learning to streamline document processing. But without metadata, data management is highly complex and most often, organizations handle unstructured data manually or with OCR (optical character recognition). However, this is time-intensive and can take up to several years to complete.

Importance of optimizing unstructured data

Organizations can get overwhelmed with the amount of unstructured data they must manage. With data growing by between 55% and 65% annually, organizations must optimize the unstructured data component pertaining to documentation when developing or creating a data management strategy.It is possible for enterprises to uncover priceless insights buried in their data and increase efficiency across the business. The sheer volume of unstructured data rapidly rising can bring increased risk to a company. Optimizing data can help with risk in contrast to holding data past retention periods, which can prove to be expensive.

Getting a grip on your unstructured data

First, organizations need to do an inventory of the unstructured data they are dealing with. Essentially, this is about its quantity, age, and type (i.e., PDFs, Word documents, Excel spreadsheets, etc.) It is also important to understand who owns the data, who can access the data, and how much it is costing to store the data.

Once due diligence is carried out, the next step is to categorize data in terms of whether it belongs to, for example, the finance or the HR department. From there, content evaluation needs to be done. During this stage, questions need to revolve around data sensitivity, adherence to GDPR regulations, or if it contains intellectual property.

Organizations also need to consider what data must be saved (i.e., for legal or tax purposes) and for how long. This is similar to categorizing structured data, but it is important to understand both kinds of data are valuable and must be treated as such.

Data classification can be automatic at a high level, but at a granular level, lines of business need to be engaged to understand the organizational requirements. A combination of good technical capabilities allied to the right tools, coupled with good business analysis, will lead to successful classification projects.

Unlocking value

Organizations need to efficiently optimize and manage unstructured data. Once data is optimized, it can be used to discover important insights that can enable the businesses to move forward. But storing more data than you need is expensive, and there are at least three major obstacles to overcome to achieve good unstructured data management. And going the manual route is just too time-consuming.

With these caveats in mind, apply the best practices noted above to your data management process. You’ll be on your way to unlocking the value of your data in a timely and cost-effective way.