When big data first burst onto the scene, it seemed everywhere in media. But we live in a fast-paced technology-driven world where the hot topic and headline of yesterday becomes old news today, and all the hoopla seems to be subsiding. According to a recent TechRepublic article it seems this theory holds true, for the article says, “a quick glance at Google search trends indicates a tapering off of interest over the past year. The hype cycle for big data, it would seem, has played itself out.”
That doesn’t mean big data has gone away—quite the opposite. According to the same TechRepublic article, “as often happens, however, just as big data has lost its hype, it’s actually accelerating in terms of real enterprise adoption.” But with widespread organizational adoption of big data technologies, why has the luster worn off? Hype, like hysteria, is only sustainable short term, and eventually everyone must get down to the very real business of doing business. And big data, though it may have lost some shine, has simply moved from novelty to normal, losing none of its challenges in the process. As TechRepublic states, “most companies are still struggling with the foundational basics of wildly variegated, fast-moving, high volume data, and are willing to pay for some help.”
It can be difficult to stay versed in the developments of the IT world, technologies evolve and new frameworks appear every other week. But the tools to ingest, prepare, analyze, and act on massive amounts of various data are readily available. The problem is that many organizations don’t realize that leveraging data requires the convergence of separate tools for tasks like data quality, data governance and analytics. If these tools work in conjunction with one another, not independently, the results are greater than the sum of its parts.
Many organizations are using numerous, varied tools from multiple vendors, relying on already overburdened IT departments to piece them together into a cohesive solution. But this can be very time consuming and it won’t be built with the business user in mind. Organizations need a single, business user-friendly platform that provides an integrated suite of data quality, governance, and analytics tools that work in accordance to enable better control over data and help solve business impacting questions. The first step is to establish data quality.
Data Quality is an important measure that businesses can use to assess enterprise information and data asset usability for strategic planning, tactical decision-making and day-to-day operational activities. Organizations, large and small, across all industries have complex data supply chains. The platform should validate data for completeness, accuracy, relevancy and consistency to ensure that all systems are in harmony and that business users have the confidence to utilize all the data at their disposal.
Data Governance can then help deliver a business-friendly perspective of an organization’s data landscape. By connecting back-end technical metadata systems to the platform, technical jargon can be translated into plain-spoken terms, removing the ambiguity faced by business users struggling to perform critical business functions. The platform should also provide users with an all-inclusive view of their data. This will allow them to easily define, track lineage of, and manage all aspects of their data assets, enabling collaboration, knowledge-sharing, and user empowerment through transparency across the enterprise.
Analytics enables data governance to improve quality and helps users make smarter, faster business decisions. The platform should enable machine learning of data quality and governance through an easy to use drag-and-drop visual interface, with data preparation and operationalization capabilities. Business users can then easily utilize sophisticated analytical algorithms without coding experience to improve data quality results dynamically. The analytical tool should allow users to train, score, and evaluate data sets with speed and simplicity. This enables automation of much of the modeling process, allowing users to focus on analysis and action rather than wasting time on data preparation.
When these capabilities are combined in one, all-inclusive platform, the burden is taken off the IT department. Users can easily collaborate across communication silos to answer questions and expedite resolutions. Business users will rejoice in being able to use simple drop-down menus to perform many tasks that often require help from IT.
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