Digital Transformation: How to Unlock the Potential of Your Company's Data

Everyone speaks about “data” but what in particular should concern you as a business leader?

Data Science is a very complex and fast-growing field that entails misunderstandings and challenges for both businesses and data specialists. In this article we want to share the knowledge we have accumulated throughout the years at expertlead and give you the know-how you need to unlock the potential of your company's data, regardless of the scale of your business or geography. Therefore it is important to get a better understanding of the relevant issues:

  • What makes Data Science so important?
  • How to ensure your firm is ready to handle its data?
  • What are the main challenges of the field and how can we tackle them?

We see that the World Economic Forum has recently stated 7 main fields or technologies driving the digital transformation today. The three of them that can easily be connected to Data Science at first glance, are Artificial Intelligence and Big Data Analytics, as well as Cloud. The reason behind this is the fact that from the total global amount of data we have, less than 1% is actually analysed, and its amount is growing at a fierce speed, producing almost 2 megabytes of data per second per person. Therefore, together with the huge popularity of the technologies that help to handle data and drive digital transformation processes via Data Science, we face some problems from a business perspective even before we start implementing them. 

According to Forbes, 90% of the companies understand the importance of Data Science and are willing to increase their expenditures for projects connected to Big Data, so there is a willingness for change but if you look at the results only a bit more than half of companies have successfully managed to implement relevant changes. How come? Reasons behind this gap could be the fact that many companies have the data, but do not know its scope and potential. Often they cannot figure out exactly what to do with this data, how to begin managing it and what specialists are required for each specific type of project. The second challenge is a lack of talent: since Data Science and related job profiles are becoming extremely popular nowadays, it becomes even harder to find the right talent. 

So, what can we actually do as baby steps, in order to mitigate these two challenges and unlock the driving power of Data Science? This graph represents a nice perspective of the possibility for evolution, the main milestones in becoming a data-driven market player and how it’s possible to challenge the issue of lacking awareness. 

Source: Stackfuel “The case for data-driven decision making” (Dr. Alexander Friedenberger)

From a company or macro-perspective, the unleashing of data power can actually already start with classic reporting techniques and move up to specific Machine Learning models used by market players such as Netflix, Amazon, Uber, Bank of America and many more, or even reaching a higher stage of Autonomous systems’ implementation. Of course it doesn’t mean that each company and its top management that plan to become a data-driven company will need to reach all stages and overcome the Chasm. However, one of the first steps should include working with raw data and eventually making it “clean” in order to identify current, obvious patterns by asking the question “What happened?”. Then it's equally important to deep-dive into possible reasons, or so-called hidden patterns, in order to get an answer to the question “Why did it happen?”. This can even go beyond the Chasm towards the analysis and control of what will happen in the future with the help of Predictive Analytics and Machine Learning for instance.

Speaking from the project- or micro-perspective, let us have a look at the data life-cycle:

 

Source: Simplilearn 

Important to mention, this is just a general overview with very basic steps. The particular stages can vary due to the scope of the project, nature of the data, its amount and complexity as well as the current team structure and eventual goals. For instance with the previous graph, we always start with Data Acquisition and Data Preparation. When you convert various data types into a common format by cleaning and structuring raw input: it doesn’t matter if you do classic reporting analysis or move to Smart Visualisation / Business Intelligence. But whether you move directly to Data Visualisation or implement Data Analysis & Data Modeling to get things into action, depends on your project. At the end of the day, it doesn't really matter how many stages you cover, eventually you'll communicate your findings, either following, or in parallel with its Deployment and Maintenance. This is particularly important not only due to the growth of data, but also due to the constant changes in terms of format, which can affect your currently existing model and result in misleading insights for the business. 

Once we identify the importance and prospective steps of possible actions, how can we tackle the lack of talent challenge? There is a very nice article on Medium called “Stop Hiring Data Scientists” and you’ll  quickly grasp  the idea behind this title. The simple answer, despite the fact that Data Scientist was called the Sexiest Job of the 21st Century by Harvard Business Review, we still see a colossal mismatch between what executives want to be delivered and who they’re hiring to do the job. 

The solution to this challenge is through the identification of the right specialist to work on your needs / project. That is why at expertlead we figured out the importance of deep-diving into the Data Science field by having a dedicated vertical for it. Our own market research as well as the exchange with data specialists and clients has identified that, generally speaking and covering a myriad of job titles, we can divide specialists into three main categories: Data Engineers, Applied Data Scientists or Machine Learning Experts and Data Analysts. Depending on the project stage or data life-cycle stage and required tech stack and tasks, there are different types of experts capable of providing end-to-end solutions. As it is quite hard to find a  jack of all trades character in the Data Science field who can cover everything with a high quality solution. 

Following the logic of the data life-cycle scheme presented before, it makes sense to briefly outline the responsibilities of these specialists starting from the Data Acquisition and Data Preparation stages, this is the role of Data Engineers. They pull all the raw, unstructured data together and optimise the systems that allow Data Scientists and Analysts to perform their work. Once the data is cleaned and structured, applied Data Scientists or Machine Learning Experts leverage their skills in math, statistics and programming to better identify patterns and derive accurate predictions via complex mathematical or Machine Learning models. Finally, Data Analysts use techniques for data handling, modeling, reporting and visualisation, in addition to their understanding of the business to drive useful insights out of it. At expertlead we have developed a special vetting process for each type of data expert to secure only the best talent for your projects. Furthermore we have supervised hundreds of Data Science projects and can use our experience to decide which data expert your company really needs.

However, to get the right specialist is only a partial solution. It is also important to continuously test and monitor implemented solutions using different techniques in order to ensure that the provided solution will eventually result in business success.    

This article is based on a talk which was held on our event How Data Science Adds Value to your Business. You can access the corresponding event recording here.

 

If you are in need of highly skilled Data Scientists reach out to our expertmatch team. Or if you yourself are an expert in your field join our freelancer community.

Apr 2020 - 5 min read

Anastasiia Prokhorova

Anastasiia Prokhorova

Senior Freelance Manager

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