Over the past decade, data and data science has gone from a hype to a key differentiator that can’t be ignored and is here to stay. Quotes like “Data Scientist is the sexiest job of the 21st century” and “Data is the new oil”, have created a bandwagon, which every company feels they need to jump on. The amount of available articles and literature on the Internet would suggest that there is enough material to clarify the concept of data science, data scientist and the benefit of the related competences to the business, but reality paints a different picture. As a data science company, we believe it’s part of the job to create this clarity for the client (in this storm of buzzwords, job titles and numerous different definitions of the same concept) and concretely explain what we have to offer to the client. This is exactly the goal of this blogpost and we’ll use a few statements (that in our eyes are misleading) as keys to our explanation.
Data Science is New!
Data is not new, and neither is science. Data and scientific analysis methods have been around for decades. The key changes that made “big data” and “data science” hypes are
- The amount of data that we generate and the speed at which we generate it from various sources.
- The technological advancements that made possible to store, process and analyse the generated data at this scale, price and speed .
Due to the increase in available data, necessary computational power and the blend with the scientific approach and methods from other disciplines, some analyses are now possible, that weren’t before. That is new, but the art of working with data has been around for ages.
My company needs to use Big Data
The use of big data shouldn’t be the objective for a company. The business needs are resolving business pains and as a company, you have decide if data can be the right tool to accomplish this. Your business needs can relate to growth of your business, optimisation of your operations, solving your business pains,… By defining a concrete goal and acknowledging that you can use data to try and tackle that challenge with a clear approach and strategy, that’s when you have the highest chance of creating true business value from data. Data in itself has no intrinsic value. Using it in the right context and with high commitment, is what creates value. Making use of big data and data science for the sake of following the hype without a purpose, will have a bigger chance of creating a competitive disadvantage, because it’ll cost money, waste precious time and potentially create a bitter aftertaste, due to the lack of concrete results.
Data science doesn’t necessarily require BIG data, because the goal of a data scientist is to find smart, relevant and useful data and insights. This also means that data science is not just for the happy few that can afford the big scale infrastructure and investments, but it can also be accessible to the SME market, if the customer is capable of scoping the analysis in a smart and cost-effective way.
My company must hire Data Scientists
The current use cases for data science in the business world can typically be categorised in two clear topics, namely optimising key business processes or finding new ways of doing business. In order to facilitate the work of data scientists in your company, true commitment to a clear data strategy and underpinning it with a roadmap for governance and architecture, are key for your data science team. Because when working with data, there is only one certainty – “Garbage IN, Garbage OUT“. A data strategy is never a big bang story and requires a true complete team effort, but believing in and committing to it is quintessential to the success and retention of your data scientist.
Because of the high demand on the job market for employees with a data science skill set, the salaries have gone through the roof, but delivered quality hasn’t necessarily followed the same path. People who call themselves data scientists, have also created a reputation of being increasingly demanding towards job content and are known to be opportunistic job hoppers. This means that hiring a data scientist is difficult, but if you don’t have enough work to staff and challenge that person full-time, then retaining a data scientist will be impossible. All these factors should be taken into account in your decision between a data science employee or consultant.
There’s only one Data Scientist profile
When browsing online job descriptions and infographics, one might start thinking there is only one profile for data scientists (Ph.D., computer science wiz, statistical wonderkid and natural business acumen). It is true that all these factors add up to a higher probability to succeed as a data scientist, but is by no means a certainty. The core of the data scientist is being a enthusiastic and proficient problem solver and having a passion for data and the skill to communicate this passion to others. Acquiring the rest of the profile depends on your own determination and eagerness to learn, because this field of expertise is constantly evolving.
Because it is important to find the right data science profile in this spectrum to match your company’s data maturity and ambition, a data science consulting firm can be a true asset as trusted advisor along your journey into this wonderful world. They usually have consultants with different skill sets and experience levels and can advise you through the stages of data maturity based on prior experience and steer you away from common pitfalls.
If your company is ready to embark on your own data journey, you want to use us as a sounding board for your data roadmap or if you want to debate this topic in general, feel free to contact us and we’ll be happy to advise you in any way possible.