How to Overcome the Challenges in Managing Big Data

At a certain point in time, it becomes important for any organization to understand the data they have been using. They should have a track on what formats are being used, where they have been used so that they can in turn control or secure it. There has been a long list of organizations in the past who have suffered from cyber-attacks and made it to the headlines. However, not just cyber-attacks an entire organization should beware of hundreds of other things that might involve data in it. Successfully managing data and implementing strategies to drive the business requirements is a challenging task and often there are no fixed remedies to handle issues.

To overcome such issues, there has to be some data management strategy inclusive of a set of policies that a firm could follow to effectively control and protect the data from all sort of threats. This could be based upon several factors such as:

•   Where from the data is pulled?

•   How much data is being utilized?

•   Who needs to access the data?

Moreover, it would then become very difficult to pick any one data management strategy that suits perfectly to the organization.

If we closely look at the firms, we can find that the data these firms hold is spread across multiple platforms and systems, including private and public clouds. There can be data audits which can help us to identify where exactly data is located and further allow centralization and good management. Using this concept of data audit, we can categorize the data and place it into its correct stage in the workflow lifecycle. Now from the lifecycle, we can think upon whether the data has to be retained, archived or deleted from the database. Through the lifecycle, we can understand the flow of data, know when it was created, and for how long it shall be retained in the system before we plan to delete it.

Also, other important factors we need to think upon are the size of the IT organization and how much it can spend to get this all sorted. There are hundreds of solutions available that can be centralized and automated according to the whole organization's strategy. The below mentioned three things can help managing data in a far better way i.e. in accordance with GDPR rules.

1. Centralizing Data

The main thing we could do is centralizing the data so that it is protected and easily manageable. This reduces the risk of possible breaches. Further, if data is present in fewer locations, there are fewer avenues for external data breaches.

2. Automating Data Management

Automation comes as a vital tool to implement policies and procedures as this takes up the process quickly and efficiently. But increasing the number of DMS (data management strategies) will also complicate things. If a certain DMS has too many things, this could become complex and even time-consuming. But automation can definitely make it human error-free.

3. Measuring Success

What's the use of implementing DMS, if at the end we land nowhere? So, while making choices for which DMS we need to go with, this factor should be majorly considered. A successful DMS is the one which ideally should simplify processes, increase the protection and visibility of data and help to drive IT efficiency.

Even after having all points considered, if we look at some static cross-industry studies, we can figure it out that on an average, less than half of an organization's structured data is actively used for making decisions. And close to 1% of unstructured data is hardly analyzed or used effectively. More than 70% of employees have access to data they should not, and 80% of analysts' time is usually spent simply discovering and preparing data for what so ever use in the future. Having different Chief Data Officers and Data Management Strategies is just a kickstart. These can neither be fully effective as a coherent strategy nor can help organize or govern an organization's information assets.