Three Big Data Mistakes Not to Make

Avoiding the Pitfalls in the Mega-Information Age

Big data is here—and it is going to keep getting bigger. By 2020, there will be eight billion people on Earth, using 20 billion devices and communicating with 100 billion connected things[1]. By that time, an estimated 1.7 megabytes of new information will be created every second for every human being on the planet[2].

Despite all the buzz, industry insights, and large investments being made, CIOs and other leaders too often lose sight of their ultimate objective. Gathering a ton of data, after all, is meaningless without the right strategy, know-how, and tools. Many organizations get off on the wrong foot, failing to connect their efforts to their core business goals and values—and taking shortcuts that may compromise results. With a little awareness, they can correct course and begin to master the data deluge.

Here are three common mistakes companies make when trying to harness big data.

  1. Failure to think differently
    What worked in the past, doesn’t always work in the present. When it comes to big data, it’s tempting to think that “big” simply means large volumes and more transactions—and apply the same strategies. Many big data analytics initiatives, however, involve semi-structured and unstructured information that needs to be managed and analyzed in a manner very different from structured data in enterprise applications and warehouses. New thinking—and a new solution set—is certainly needed to gather, filter, store, integrate, and leverage a considerable percentage of your big data.
  2. Failure to answer business questions
    There’s so much data being generated and collected, it can be overwhelming. CIOs and IT leaders too often lose sight of business requirements and their ultimate business objectives, because they get caught up in the big data infrastructure planning. Big data teams need to work closely with business leaders to ensure tech aligns with corporate goals. Smart companies will start with the business questions they have and then look to the data for answers. It’s a focused approach. Doing it the other way—looking at your huge pile of data and trying to figure out what it all means—is a surefire waste of time and effort.
  3. Failure to invest in the right technology
    As legacy databases continue to increase beyond their limits, and more companies face risks from data security to storage, a range of attractive solutions have entered the market. Going for the quick fix, the cheapest option, or the one with the shiniest bells and whistles will not serve your interests and could compromise your data goals. Due diligence is needed to select the right solution that will retire outdated applications, reduce costs, and allow you to leverage your data to its maximum value. HPE, a trusted name and trailblazer in the big data world, has recently introduced its Structured Data Manager. It takes a unique approach to storing, managing, and extracting value from data based on a robust selection of pre-built integrations to cloud storage, comprehensive information management systems, and high-performance analytics. This is the kind of data management solution that will offer the best return on your investment.

For more information and guidance on big data best practices, visit Orasi Software.  

[1] https://www.hpe.com/ca/en/solutions/empower-data-driven.html?jumpid=ps_xrzt3yevqf_AID-510018643&gclid=CLGvzqqwuc4CFUddMgodZtUGwQ&gclsrc=ds

[2] http://www.forbes.com/sites/bernardmarr/2015/09/30/big-data-20-mind-boggling-facts-everyone-must-read/#2bf8922a6c1d

About Orasi: Orasi provides quality engineering integration to our partners and customers, acting as the customer’s single point of contact throughout the full software lifecycle. Our team examines each customer’s needs and selects the best mix of software quality engineering tools and processes to fit those needs.

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