Capping IT Off

Capping IT Off

Opinions expressed on this blog reflect the writer’s views and not the position of the Capgemini Group

Analysis of Data is Not the First Step

with contributions from Todd Heil and Ed Patterson

Data Analysis is the practice of ordering and organizing data into information, and identifying and delivering that information to the right decision maker so that useful business insight can be gained, and well thought out decisions made. Today’s forward looking organizations are striving to achieve actionable intelligence – true business intelligence that answers current challenges with near real time answers and insights to support bold decisions. Before this analysis can be done, however, business must understand the data that it collects and stores. This data has to be identified, documented, acquired, organized, filtered, and cleansed before it offers real value to the end business consumer.

The application of methodologies such as Master Data Management and Data Quality Management are critical principles in a corporation’s objective of defining a “single version of the truth”. Master Data Management is the process of defining the core data attributes of your business and centering data gathering, analysis and decision making around these attributes. This process must be dynamic to properly adapt to a changing business environment. A well structured master data management program ensures that as a business transforms into a true data consumer, the intelligence that is generated is logically organized in support of the business’s most critical needs. Data Quality processes, tools, and initiatives cannot be overvalued either, as without data quality, the mantra of “garbage in, garbage out” becomes the backbone of how your business fundamentally functions and relegates the business to the group of ‘also rans’ in an industry. These programs represent only two elements of a well thought out, well disciplined and properly supported strategic business intelligence program that is used as a fundamental element of all industry leading businesses.

Raw data does little to provide true insight into the health or direction of an organization. As businesses recognize and nurture their data as an asset rather than just “buckets of ones and zeroes”, implementing a well conceived business analysis initiative will help drive business decisions and can reveal important facts about their customers. Uncovering trends that might not otherwise be seen and supporting critical business decisions that are made by finance, sales, marketing and other departments can transform a business from an also-ran into a market leader. The ability to make informed business decisions relies heavily on having the right data that is organized, accurate, and meaningful, and the strength and discipline to implement tough process changes so that effective and accurate analysis can be performed. Although any change introduces risk and resistance, implementing a well conceived, properly supported data analysis program provides rewards and opportunities that no business can afford to ignore in today’s turbulent business environment.

In a world where Big Data challenges are becoming more and more prevalent, managing the structure and quality of this large volume of complex information is critical to maintaining the integrity of any business analysis which is being performed. Many of our (Capgemini) customers across several different sectors/industries are facing these challenges today and working arduously (with support from our Business Information Management team) to leverage analytics to improve product performance, drive increased profitability, enhance customer experience, and validate marketing spend. Communications firms are utilizing geo-location data as a new mechanism for providing targeted marketing opportunities for clients. Media and Entertainment organizations are mining complex, unstructured streams of social data to measure consumer sentiment of new releases based on response to trailers. Utilities are proactively collecting consumption data and creating time-based incentives to normalize utilization of power grids. Without mastering the core attributes of your internally generated data, Big Data analytics can’t be developed in a manner which adds any strategic value to the business. In addition, application of Data Quality principles and tools is critical to effective use of these new, complex Big Data sources.

The impact of these principles can be seen in the marketplace as many leading data software firms are placing increased focus on both MDM and Data Quality. To point, Gartner now has created it’s “Magic Quadrant” analysis for each of these subject areas as the marketplace floods with new tools and concepts. Many industry leaders such as Informatica , IBM, SAP, and Oracle have invested heavily in these sectors.

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