Undoubtedly, 2017 has been yet another hype year for machine learning (ML) and artificial intelligence (AI). As ML and AI become increasingly ubiquitous in many industries, so does the proof that advanced analytics significantly improve day-to-day operations and drive more revenue for businesses.
Yes, it’s true – enterprises worldwide have shown us time and time again that there is major potential for industry change with ML and AI. But in order to bring about that change, there must be strategy involved. It’s not enough to assemble a large data team and expect the results to come; in fact, becoming a truly data-driven enterprise at the core has proven to be more of an organizational challenge than a technical one. Becoming successful with data requires collaboration across teams, placing new concepts – such as reusability and reproducibility of data models – at the heart of the business.
Data-Driven Organizations – The German Example
While these technological and organizational evolutions are happening across the globe, there are several areas in which they are gaining particular momentum. One is in Germany, where the previous Minister for Economic Affairs, Sigmar Gabriel, announced in 2016 that “data is the commodity driving our digital age.” As dawn breaks on a new calendar year, it seems truer than ever. According to a recent study by Bitkom Research and KPMG, approximately 60 percent of German companies have already managed to either reduce risk, reduce costs or increase revenue through the use of data science (including ML and AI). The Mittelstand is slowly but surely understanding its value, and big data technologies are inching towards widespread adoption.
Yet, despite this revolution, 77 percent of German companies still rely on small data tools (like Excel and Access) for ad-hoc data analysis. There’s still a long way to go, but this figure is down 10 percent compared to 2015, which shows promise. Businesses that were ill-equipped in the past to manage large amounts of data are in the process of “gearing up.” This trend coincides with a rise in the adoption of data science platforms.
What are data science platforms, exactly? Well, in order to scale, data teams need staff, structure, efficiency, automation and a deployment strategy; data science tools facilitate these requirements. On top of scalability, however, they provide the tools necessary for a data team to easily manage ever-increasing volumes of data, innovate in a competitive market, move away from error-prone ad-hoc methodology, easily reproduce processes and data projects, and – perhaps most importantly with the coming of the EU General Data Protection Regulation (GDPR) – have proper data governance and permanence in place.
The Optimal Use of Data
Businesses are learning firsthand the famous adage whereby 80 percent of a typical data science project is sourcing, cleaning and preparing the data, while the remaining 20 percent is actual data analysis. They are now taking steps to bring their organizations to a more optimal 50/50 balance. Many see data science platforms as the answer and the future, realizing that having the right tools for the right job is critical to success.
Data processes often span multiple roles, from business to compliance, risk management to data science itself. So having the right tools and staff members for complex systems ensures efficient collaboration and also, very importantly, data protection (we said it once and we’ll say it again: GDPR is on its way). As the amount of data grows exponentially, setting up a unified data strategy to scale within the company is becoming high priority.
The Importance of Data Analysis
Data analysts play a key role in the midst of all this frenzy to help shape data-driven decision-making. Market demand for analysts and machine learning expert positions is rapidly increasing. Since 2015, there has been a five-fold increase in the number of job postings advertising data science. New types of data, tools and analytical methods are all pushing the jobs of both data scientists and analysts into pioneering, exciting directions. Data analysts are moving more and more into the space of machine learning.
This is an incredible opportunity for analysts to hone and develop their skills. In order to help data analysts hop into the realm of machine learning, Dataiku, which provides a collaborative data science platform software, has put together a free, illustrated guide. Analysts can expect various content from the guide, such as:
- Machine learning concepts for everyone
- An introduction to key data science concepts
- Top prediction algorithms
- How to evaluate models
- Introducing the K-fold strategy and the hold-out strategy
- K-Means clustering algorithm in action
- Documentation for further exploration
All in all, 2018 is headed for more exciting innovations in data. Analysts from all horizons shouldn’t hold back in exploring the latest in machine learning and AI. Join the data revolution and hop on board for a successful year ahead!
Download the free, illustrated machine learning guide for data analysts.