The first wave of Master Data Management (MDM) started in the early 2000s. Early adopters from a few industries which included Banking, Insurance, CPG, and Retail started implementing MDM for customers, products, and a few other master data domains. Over time, MDM became mainstream as companies from all industries realized the need for MDM as part of their data strategy to resolve data inconsistencies across multiple systems and establish a single version of the truth. Over the years, there have been many successes but plenty of failures as well, due to the lack of skills and a proper understanding and positioning of MDM solution versus other systems.
Our clients frequently complain that their understanding of their business is hampered by the fact that much of their vital data resides as ‘dark data’ in silos that don’t reside on traditional IT infrastructure (system data).
Financial frauds are expensive, not just in terms of the money involved, but also for poor customer experience and loss of customer trust. There are many types of financial frauds, such as credit card frauds, e-commerce frauds, insurance frauds, to name a few.
Data is generally considered high quality if it is "fit for its intended uses in operations, decision making, and planning" .
In the last 50 years, the shift from an industrial economy to an information economy has caused data to become increasingly important. This has highlighted the costly impact that poor quality data can have on a company's financial resources . Poor data quality contaminates the data in downstream systems and information assets, which increases costs throughout the enterprise. Moreover, customers' relationships deteriorate as a function of poor quality data leading to inaccurate forecasts and poor decision making. Recently, Harvard Business Review reported that out of 75 companies sampled, only 3% had high-quality data.
The Covid-19 pandemic has taught us all valuable lessons on a personal, societal, and global level. Over these past few months, I've done some self-reflecting of my own, but I also wanted to reflect on what Covid-19 will mean for businesses and IT, especially enterprise intelligence, the learning at speed.
What is an Enterprise Data Bus (EDB)? An EDB is a collection of data management and analysis tools that are installed and orchestrated so that the corpus of enterprise data is made available for analysis, and business value creation. An EDB is the analytics node of the data fabric. It can be used as an integration hub and connector layer of the data fabric. An EDB embraces the DataOps concept by allowing an agile method for automation and orchestration of the enterprise’s data journey, followed by Data from creation to its eventual use for deep analytics, data science, and automated business decisions.
Communication is a key skill in any field. Without communication, we would be hard-pressed to drive change in an organization. We need to communicate ideas well to ensure that the people that help us do what we do, can help us do what we do. One of the key functions of any data scientist is to encourage data-driven change to effect some positive collective outcomes (increasing revenues, reducing costs, improving resiliency). If you can’t communicate the work you’ve done in a concise, meaningful, contextualized way,…you may fail in making your work actionable. The right people responsible for driving change won’t get to onboard your train, and your data science will go nowhere. No person is an island. We need the help of thousands of people to do what we do effectively, and to do that, communication is an absolute key.
“I cannot live without brain-work. What else is there to live for?” – Sherlock Holmes
I saw this challenge (minus the solution) being passed around in a WhatsApp message, and after solving it, it struck me as a fantastic way to illustrate the difference between data, information, knowledge, and insights, under the pretext of not getting shot.
Let’s get started, shall we?
“My name is Sherlock Holmes. It is my business to know what other people don’t know.” – Sherlock Holmes
Many people think that Data Science looks like this:
In many meetings, I’ve been asked by customers, “How do you demonstrate an ROI for an MDM solution to manage customer data.” They all get the merits of what MDM can do - a single source of truth for customer data that is the foundation of many customer-centric business initiatives.
But how do you create a business case for this?
To start, we need some basic metric assumptions that will be useful for our calculation: