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8 Steps to Maximize the Potential of a Big Data Strategy

8 Steps to Maximize the Potential of a Big Data Strategy

MaxThePotentialAn article from Spotfire’s Business Intelligence blog shared eight steps firms must take to successfully exploit the potential of big data as reported by Gartner analysts.

The Gartner analysts suggest that business owners and IT:

1. Recognize that big data projects are different than traditional IT approaches. Because big data projects focus on acquiring and preparing information rather than the functionality of the data, this change can strain past approaches to enterprise architecture and project management, the article notes.

“Additionally, big data initiatives require a degree of financial rumination and discipline focused on the question, ‘What value can we generate from this data, and is it more than it costs us to accumulate, administer and apply it?’ The outcome of big data projects can be uncertain,” according to the article. “With time being money, how quickly can your organization get from focused experimentation that yields insights or innovations to its implementation and institutionalization?”

2. Ask big questions of big data. Big data offers the opportunity to ask questions that go beyond those that look backward at historical data. “[Business executives] should ask questions that make full use of broader, deeper and more real-time data and, if answered and acted upon, could have profound effects.

3. Educate leadership. Many business leaders may still be resistant to relying on data for decision making, the article points out. Thus, business leaders and IT should educate executives in basic statistics, risk planning and avoiding “group think.”

4. Balance costs and benefits. “One important scheme for tipping the balance of big data benefits to outweigh its cost is ensuring that the data serves multiple business purposes,” the article notes. “Compiling, hosting and processing petabytes of data for a single business process rarely makes for sound financial fundamentals or good use of scarce skill sets.”

5. Build a strong infrastructure. Many traditional technologies and tools were not designed for the volume, variety and velocity that define big data.

“IT generally defaults to extending existing systems capabilities to meet new processing demands,” according to the Gartner analysts. “But since generating business value from big data [is] so urgent and potentially impactful, merely waiting on technology evolution is sometimes not an option. Investing in new purpose-built technologies may be necessary.”

6. Embrace risk. Big data strategy must include governance, controls and contingency plans because big data sources often include sensitive or proprietary information that could be vulnerable to mishandling or misuse, the article notes.

7. Expand the skills base. Big data demands workers with skills that go beyond traditional query and reporting capabilities. Companies must add employees with skills in: predictive analytics, data mining, visualization, complex event processing and natural language query.  

Understanding how to apply these capabilities demands a range of skills, but the new talent required to manage and leverage these information assets is in exceptionally short supply,” the article notes. “These skills include data integration and preparation, business and analytic modeling, collaboration and communication, and creativity.”

8. Change the organization. Big data can require IT organizations to change how they operate.

“Most are badly equipped to deal with an individual business unit’s desires or attempts to manage and leverage big data on its own – often outside the context of traditional data warehouses and business intelligence efforts,” according to the article. “Because big data initiatives are especially demanding on the partnerships between IT and the core business, it’s essential that both groups weigh the necessary strategies and planning to maximize the organization’s return on big data initiatives.”

Read the original article here.


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