Drive Strategic Initiatives with Big Data
Enterprises today are achieving sustained competitive advantage by deriving quality business insights from the humongous data stores at their disposal. In addition to gaining valuable customer insights and forecasting the future, big data gives businesses the option of creating new opportunities. With overwhelming amounts of enterprise data being generated every second, enterprises must have a better comprehension as to which type of information is mission-critical to their business intelligence (BI) initiatives.
Focus on the Business Objectives
With the availability of an extensive range of big data-centric software and hardware solutions and open source data platforms, newer analytics applications are being introduced into the marketplace that leverage big data. A lack of strategic vision and organizational alignment usually impedes big data technology adoption. So before implementing a big data project, an enterprise must re-evaluate its operations, implement suitable technology solutions, and prepare beforehand to face the challenges that may arise. Ideally, enterprises must align the goals of their big data initiative with their business objectives to launch and manage a successful big data strategy. An ideal big data team should be a cross-functional mix of business analysts, data quality engineers, and product managers who can correlate the analytical efforts with the enterprise’s business objectives.
Build in-house Expertise
The structuring of big data initiatives often falls short when it comes to tackling the challenges associated with staffing the enterprise to embark on the big data journey. A key concern that must be addressed before developing an enterprise big data vision is the approach to be followed while procuring and developing the internal skills ranging from management to technical roles. Technologies such as Hadoop might be affordable but the actual expenses start adding up once the enterprise puts together the required human resources—a team of experienced data science professionals.
Eliminate Data Silos
Businesses must focus on ensuring the flawless functioning of their data warehousing and analytics systems, as laid out in the clearly stipulated service-level agreements. An unstable system may result in interrupted data-processing workloads that would again translate into redundant data, incomplete information, and poor data quality. Also, to facilitate a quick data transformation process, it is important to ensure optimal levels of data quality while upgrading to a more advanced version of the analytics system that offers an interactive user interface for end users.
Cultivate an Analytics-driven Culture
Enforcing a more data-driven culture should be a top priority for businesses that are keen on investing substantially in procuring essential human resources—data scientists—and the latest versions of technologies such as Hadoop. This initiative should be powered by an analytics-driven culture instilled enterprise-wide from the top down. By making it known that the enterprise has invested in analytics as the foundational technology, the C-suite can champion the cause and inspire IT teams and business executives to follow suit. In addition to boosting the firm’s commitment to analytics-driven decision making, this will also ensure big data best practices are preached by the executives so that the subordinates can follow their lead.
Build a Robust Data Governance Program
Another overlooked and yet mission-critical aspect of any kind of big data initiative is building a foolproof data governance program. An efficient data governance program is key to ensuring the hygiene of data along with enforcing its integration, organization and consistent labeling throughout the initiative. For enterprises operating or planning to move into a real-time environment, the automation of processes and construction of architectures that transcend the typical monolithic data warehouse is highly advisable.
With the proliferation of mobile devices such as smartphones, enterprises must realize the value of adopting integrated data management techniques that fuse together IoT, big data, cloud and cybersecurity. Additionally, bridging the chasm between accumulating user data and deploying it for meaningful insights will enable the customer-facing organizations to offer their customers a seamless experience.
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