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.
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 [2]. 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?
Many people think that Data Science looks like this:
As your enterprise moves toward being data driven, the ability to derive a domain ontology from your company’s data will become ever more important. In order to move to this deep analytical process, it is important to understand the amount of data required and the state the data must be in before you should attempt any deep analytics studies.
The goal of data science is to translate ALL business problems into scientific problems which can be managed and/or improved in a systematic data-driven way. It is the job of a data scientist to do exactly this. All business problems will benefit from the application of a more scientific and data-driven approach.
When you read the marketing spin on Big Data and the tools available today, you may deduce that there is much upside and not much downside to implementing a Big Data project. Nevertheless, you will quickly find that this is not the case. It’s not as simple as typing “apt-get install Hadoop” into a Linux command window, everything installs, you ask it complex questions, and it gives you sage advice. It is tough to get a Big Data project working.
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