Posts about Analytics and BI

Not all “multidomain” master data management (MDM) solutions are the same. Here are the differences and why you need to know.

When selecting solutions to help support your master data management and data governance ambitions, one of the many aspects you are likely to evaluate will be support for “multidomain” MDM. Your initial inclination is almost certain to be selecting a solution that can “do-it-all.” In many cases, this will be despite the fact you currently have a business use case for only one data domain; customer or product data, for example. A single solution that could do more in the future would, of course, be desirable, to make more efficient use of available resources.

Together, small data and multidomain MDM reveal new insights into your business

Over the past few years working in data governance, I have encountered many projects aiming to re-write decision making processes using ”big data.” Data lakes, predictive analytics, AI and a cacophony of other acronyms have for many people veiled the complexity of big data, which pledged to deliver the four golden eggs of data insight; 1) better decision making, 2) happier customers, 3) freedom from risk and 4) identification of new opportunities.

Unified and enriched customer information can enhance your data analytics and provide actionable customer insight for corporate and investment banks

Relationship managers need insightful customer information upon which they can take action. Sometimes a single piece of unique information can give relationship managers in corporate and investment banks the edge they need to decide how to move an opportunity forward, or indeed avoid being exposed to risk. The availability and trustworthiness of that piece of information relies on the quality and richness of the data that is being analysed.

In the internet’s infancy, before search engines and browsers, you had to know exactly where to look to find the information you wanted.

With the emergence of search engines, the entire wealth of information became available at our fingertips just by inserting a keyword. Google’s most significant achievement involved making sense of big data served up from over a billion websites globally according to user preferences, location and history. Google simply and elegantly integrated the world wide web to deliver information with speed and relevance for the user.

A sound analytical strategy encourages activity and embraces a self-service methodology but also provides guidance and oversight from those close to the data.  This governance will be instrumental in preventing data quality issues, superfluous analyses and misguided decisions.  Enter master data management (MDM).  The technologies and processes underlying a typical MDM strategy not only provide oversight and access control, but also enable companies to track the origin and lineage of data to ensure safety and consistency in the decision process. 

Keeping track of your physical assets: devices, tools, equipment and real estate, to name a few, already requires managing and maintaining large amounts of information. Where are your assets? Who is using them? These are typical questions companies encounter on a daily basis and their answers are critical to maintain efficient operations. Errors in this information and delays in accessing asset data can result in loss of productivity and higher costs. But what happens when your assets start generating data of their own?