Real-World Big Data Management Pitfalls that can be Mitigated through Data Science Training




As the applications of Big Data expand, more companies are moving towards digital transformation. Data is an asset (and perhaps the most valuable one at that), one that can be used in a multitude of ways. For instance, it allows you to plot business milestones and your strategic trajectory for the future. If leveraged properly, big data most certainly offers businesses a competitive advantage over their competitors.

According to Brian Hopkins, a Forrester analyst, as of last year, 41% of firms are expanding their implementation into big data, have implemented it, or are in the process of implementing it. Additionally, another 26% have realized the merits of big data and intend to branch out in this area in the next 12 months. While the remaining firms have no plans whatsoever.

For big data digital transformation to be a success, don't let it be a resource and money-hogging project. Find ways to create competitive advantages for your clients and customers through data insights and analysis.

Obviously, investing in big data, synchronizing it with your existing systems, and providing appropriate data science courses to your employees is a sizable venture. Followed closely by the cost of administering the data, acquiring and applying it. This process can only be as smooth as you make it, and the best way to do so is by avoiding the pitfalls and providing appropriate Data Science training to your administrators and employees.

Venturing into big data is certainly lucrative but it isn't without its challenges. Companies that are seeking digital transformation can avoid the following pitfalls through data science training.

Failing to give Data a Purpose

Firms that have most success in Big Data are ones that are open to implementing new and improved technologies across the board. Implementing big data digital transformations isn't something firms do just for the sake of doing it, it is done by experienced executives who clearly understand how they would leverage the technology to create competitive advantage and distinction.

Big data is all about the generation of actionable insights that allow informed decision making. A firm cannot afford to lose sight of the purpose of the data, if they do they'd end up wasting considerable time and resources, find out more about data management in our previous blog.

Firms need to have clear idea in what advantages they wish to gain with data, and a plan on how to precisely gain the insights they want. All of which comes way before deciding to implement Big data transformations.

Forgetting to Monetize Data

Most firms wrongfully assume that they'll require a set of data only once, so they discard it after use. That mindset needs to change as every spec of data requires that you think about it broadly and find ways of using and monetizing data over and over again.

Monetizing the data you have, isn't just about licensing or selling it, but all the ways you can generate economic benefits from it. Gartner, Inc. is a global research and advisory firm, has hundreds of analyzed big data implementation cases. None of which are there to create a pretty pie chart but more focused in driving economic benefits and improved business solutions through big data.

Failing to Look Beyond your Industry

Enterprises and firms often only look within their industry to see what's being done in big data. Why be follower and not a leader? Rather the executives need to look towards other industries to see how they leverage their data to stay ahead of the curve.

The industries you can look at range from retail to healthcare and figure out how you can replicate and implement those ideas and strategies within your own firm. This way you can get a jump on your competition.

Dismissing External Data

Firms are well aware of the fact that there are many external sources to collect data from. But the executives are already so focused on the data the internally collected data, that they do not realize the benefits they'd get from bringing data in from organized data providers, scrapping web content and obviously data from social media.

In addition, most firms already work with various third-party partners that could provide them the data they need. Companies that showing success with their Big data digital transformations by thinking out of the box are those that are grabbing data from freely available sources and leveraging them to their own ends.

Forget to Inventory the Data

Many firms that a plethora of data and data types in their information assets, however all this information isn't inventoried, so they don't know precisely what they have in store to use. Some firms even if they know what they have, they don't measure it nor do they know what the potential economic value of their data is.

Firms need to name, measure, and value the data they have on hand, as that is the crucial part of managing your information assets. Inventorying should be job number one, two, and three. same as all other assets of a firm.

Centralizing Ownership of Data

As part of the standard operating procedure and governance, firms go about establishing of owners of the data they have on hand. This is an important step towards responsibility and accountability of information assets that could potentially be worth thousands of dollars. While this step is crucial it could also perpetuate something that could be equally problematic, and that is "information hoarding".

If the data is owned exclusively by the IT, it would result in a pitfall because analysis of big data is something that done by entire teams on a high-end digital infrastructure. If access to the data is restricted, that would significantly hinder your big data transformation significantly.

Overcoming these pitfalls won't be a monumental task as long as you have a team that has had the appropriate data science training.

 

About The Author
Associate Instructor

Owais Rashidi

Owais is an associate instructor at QuickStart having prior experience of doing projects in .Net, SQL Server, SSIS, Data warehousing and Business Intelligence. He has done Bs in Enterprise Resource Planning (ERP) which is a unique blend of both Software Engineering and Business Administration. And is also configuration and implementation of SAP core modules.

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