Article | May 17, 2021
One approach for better data utilization is the data fabric, a data management approach that arranges data in a single "fabric" that spans multiple systems and endpoints. The goal of the fabric is to link all data so it can easily be accessed.
"DataOps and data fabric are two different but related things," said Ed Thompson, CTO at Matillion, which provides a cloud data integration platform. "DataOps is about taking practices which are common in modern software development and applying them to data projects. Data fabric is about the type of data landscape that you create and how the tools that you use work together."
Article | January 6, 2021
As the organizations go digital the amount of data generated whether in-house or from outside is humongous. In fact, this data keeps increasing with every tick of the clock.
There is no doubt about the fact that most of this data can be junk, however, at the same time this is also the data set from where an organization can get a whole lot of insight about itself.
It is a given that organizations that don’t use this generated data to build value to their organization are prone to speed up their obsolescence or might be at the edge of losing the competitive edge in the market.
Interestingly it is not just the larger firms that can harness this data and analytics to improve their overall performance while achieving operational excellence. Even the small size private equity firms can also leverage this data to create value and develop competitive edge. Thus private equity firms can achieve a high return on an initial investment that is low.
Private Equity industry is skeptical about using data and analytics citing the reason that it is meant for larger firms or the firms that have deep pockets, which can afford the revamping cost or can replace their technology infrastructure. While there are few private equity investment professionals who may want to use this advanced data and analytics but are not able to do so for the lack of required knowledge.
US Private Equity Firms are trying to understand the importance of advanced data and analytics and are thus seeking professionals with the expertise in dealing with data and advanced analytics. For private equity firms it is imperative to comprehend that data and analytics’ ability is to select the various use cases, which will offer the huge promise for creating value. Top Private Equity firms all over the world can utilize those use cases and create quick wins, which will in turn build momentum for wider transformation of businesses.
Pinpointing the right use cases needs strategic thinking by private equity investment professionals, as they work on filling the relevant gaps or even address vulnerabilities. Private Equity professionals most of the time are also found thinking operationally to recognize where can they find the available data.
Top private equity firms in the US have to realize that the insights which Big data and advanced analytics offer can result in an incredible opportunity for the growth of private equity industry. As Private Equity firms realize the potential and the power of big data and analytics they will understand the invaluableness of the insights offered by big data and analytics.
Private Equity firms can use the analytics insights to study any target organization including its competitive position in the market and plan their next move that may include aggressive bidding for organizations that have shown promise for growth or leaving the organization that is stuffed with loads of underlying issues.
But for all these and also to build careers in private equity it is important to have reputed qualification as well. A qualified private equity investment professional will be able to devise information-backed strategies in no time at all.
In addition, with Big Data and analytics in place, private equity firms can let go of numerous tasks that are done manually and let the technology do the dirty work. There have been various studies that show how big data and analytics can help a private Equity firm.
Article | December 23, 2020
Nowadays, everyone with some technical expertise and a data science bootcamp under their belt calls themselves a data scientist. Also, most managers don't know enough about the field to distinguish an actual data scientist from a make-believe one someone who calls themselves a data science professional today but may work as a cab driver next year. As data science is a very responsible field dealing with complex problems that require serious attention and work, the data scientist role has never been more significant. So, perhaps instead of arguing about which programming language or which all-in-one solution is the best one, we should focus on something more fundamental. More specifically, the thinking process of a data scientist.
The challenges of the Data Science professional
Any data science professional, regardless of his specialization, faces certain challenges in his day-to-day work. The most important of these involves decisions regarding how he goes about his work. He may have planned to use a particular model for his predictions or that model may not yield adequate performance (e.g., not high enough accuracy or too high computational cost, among other issues). What should he do then? Also, it could be that the data doesn't have a strong enough signal, and last time I checked, there wasn't a fool-proof method on any data science programming library that provided a clear-cut view on this matter. These are calls that the data scientist has to make and shoulder all the responsibility that goes with them.
Why Data Science automation often fails
Then there is the matter of automation of data science tasks. Although the idea sounds promising, it's probably the most challenging task in a data science pipeline. It's not unfeasible, but it takes a lot of work and a lot of expertise that's usually impossible to find in a single data scientist. Often, you need to combine the work of data engineers, software developers, data scientists, and even data modelers. Since most organizations don't have all that expertise or don't know how to manage it effectively, automation doesn't happen as they envision, resulting in a large part of the data science pipeline needing to be done manually.
The Data Science mindset overall
The data science mindset is the thinking process of the data scientist, the operating system of her mind. Without it, she can't do her work properly, in the large variety of circumstances she may find herself in. It's her mindset that organizes her know-how and helps her find solutions to the complex problems she encounters, whether it is wrangling data, building and testing a model or deploying the model on the cloud. This mindset is her strategy potential, the think tank within, which enables her to make the tough calls she often needs to make for the data science projects to move forward.
Specific aspects of the Data Science mindset
Of course, the data science mindset is more than a general thing. It involves specific components, such as specialized know-how, tools that are compatible with each other and relevant to the task at hand, a deep understanding of the methodologies used in data science work, problem-solving skills, and most importantly, communication abilities. The latter involves both the data scientist expressing himself clearly and also him understanding what the stakeholders need and expect of him. Naturally, the data science mindset also includes organizational skills (project management), the ability to work well with other professionals (even those not directly related to data science), and the ability to come up with creative approaches to the problem at hand.
The Data Science process
The data science process/pipeline is a distillation of data science work in a comprehensible manner. It's particularly useful for understanding the various stages of a data science project and help plan accordingly. You can view one version of it in Fig. 1 below. If the data science mindset is one's ability to navigate the data science landscape, the data science process is a map of that landscape. It's not 100% accurate but good enough to help you gain perspective if you feel overwhelmed or need to get a better grip on the bigger picture.
Learning more about the topic
Naturally, it's impossible to exhaust this topic in a single article (or even a series of articles). The material I've gathered on it can fill a book! If you are interested in such a book, feel free to check out the one I put together a few years back; it's called Data Science Mindset, Methodologies, and Misconceptions and it's geared both towards data scientist, data science learners, and people involved in data science work in some way (e.g. project leaders or data analysts). Check it out when you have a moment. Cheers!
Article | March 30, 2020
Most businesses do not have contingency or business continuity plans that correlate to the world we see unfold before us—one in which we seem to wake up to an entirely new reality each day. Broad mandates to work at home are now a given. But how do we move beyond this and strategically prepare for—and respond to—business implications resulting from the coronavirus pandemic? Some of our customers are showing us how. These organizations have developed comprehensive, real-time operational intelligence views of their global teams—some in only 24-48 hours—that help them better protect their remote workforces, customers, and business at hand.