Four Steps to Reducing Big Data Barriers

Four Steps to Reducing Big Data Barriers
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Companies that know the importance of harnessing insight from analytics—but have not started an initiative—may be intimidated by the early success touted by other companies that have embraced Big Data.

But, be mindful that competitors may also have just begun analytics initiatives—and your company should focus on eliminating common barriers to getting started, advises a new article from MIT’s Sloan School of Management.

According to the article, “The good news is that, if you know your organization needs to improve its analytical capabilities, you may not be that far behind everyone else. Despite our distorted impressions, many others are in similar situations, relying only on managerial intuition or basic spreadsheets instead of complex prescriptive models that use massive, streaming, unstructured data.”

Sloan advises companies to focus on two areas—reducing the hurdles to launch an analytics initiative and augmenting the skills needed to successfully sustain a program.

Sloan suggests that companies take four critical steps when launching analytical capabilities:

  1. Identify examples of analytical models built using real data from existing tools in the company. “It is much easier for people to create valuable models by extending and modifying; starting from scratch is tough.”
  2. Create data descriptions, guidelines, examples, templates, and a modifiable repository to avoid duplicating efforts.
  3. Identify the most useful data for the organization, and prepare it to be used by existing tools so end users will not be required to import data.
  4. Predict organizational concerns associated with data-driven decision-making. “If there are rules or restrictions associated with some data, spell them out so that people have realistic expectations about what can be done. Silos and restrictions may be unavoidable; better to know before plodding forth.”

Furthermore, the article suggests that companies take three steps to bolster the analytical skills to fuel growth including:

    • Provide resources and content specific to the tools being used to help employees build knowledge.
    • Support experimentation. “Encourage running with scissors (or at least jogging),” the article noted. “Give people some time to experiment, take risks, and mess up—then use the experience to add to the organizational learning by including it in your collaborative tool.
    • Provide a forum to surface and share employee skills sets and interests.

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