Beyond the data hype: how to turn your data into profits

Without the right context and analysis, data holds little value.

Data seems like the new magic word that can eliminate all your company’s problems. But, as many CIOs will confirm, the data itself is worthless. It is valuable only within the right context and when guided by analysis, leading to new insights, preferably actionable insights. But how do you select the right platform for data collection and analysis? And how do you translate that data into useful, actionable information? Here I lay out a broad roadmap to help you start turning your data into profits.

The most important trends in IT right now all involve data. From big data to Machine Learning (ML), Artificial Intelligence (AI) or Internet of Things (IoT), they are all about collecting large amounts of data. Suppliers of hardware and software are taking advantage of this fact and are coming out with products to capture, collate and present such data. But it can be very difficult to create a clear business case. Big data, ML, AI and IoT are still largely unexplored areas for most companies, especially if you're not a Google, Facebook or Amazon. 

The average organization can do better by first setting up a business analytics platform that can manage and analyze the currently relevant data. A cohesive business analytics platform is a start, and definitely a requirement if you want to get in on these promising new technology breakthroughs in time. 


If you see through all the hype, the value of data lies in the algorithms, regardless of volume. With these mathematical models we can combine data, relate and manipulate, to come out with useful insights. These algorithms drive the Google search engine, the Netflix recommendations, the digital voice assistants and the stock market and, in time, also the average business. The problem in many non-digital organizations is that the available business data is rarely managed with a holistic approach. 

Departments like sales, marketing, production and logistics often use separate systems and analytical tools, so their data cannot be simply combined for analysis. With time, data siloes proliferate and run out of control. Companies should first solve this problem, before they are tempted to invest in hypes such as big data and machine learning. Separate analytical tools should be replaced by a comprehensive business analytics platform which can access and interpret all key underlying data sources. This is the key towards building a future-proof business analytics platform. 

Insights from big data

When building a future-proof platform for data analysis, it pays to keep future applications in mind. Using big data might not be attainable right now, but it might be in the near future. Imagine how you would eventually use large amounts of data from social media, customer interactions and the Internet of Things to your benefit. 

You could, for instance, increase your understanding of customers’ needs by analyzing their social media posts or interactions with your company in a more holistic way. Or you could use all kinds of data from IoT devices to precisely monitor and control your company’s operations. Will this knowledge improve the way you service your customers or how the company operates? If the answer is ‘yes’, then do your research and start working on a business case to support further investments. Alternatively, you could also conclude that these new technologies are still too expensive for the potential benefits they offer. That’s fine too, since it prevents you from jumping into solutions you don’t really need (yet), and maintain your focus on the basic premise of getting value from traditional BI.  

Machine learning and AI

Other futuristic technologies that should be on your company radar are machine learning and AI. Both can theoretically be used to great benefit, but the question remains: is the case for your company right now? Not unlike big data, machine learning has a wide variety of applications that cannot be categorized easily. It can for instance be used to optimize processes, improve text, speech and image recognition, but also for fraud detection or improving data security. Furthermore, machine learning requires a lot of data and processing power, and people skilled in data science to set up the systems and do the analyses. Not every company has the scale or business model to support machine learning and benefit from this high-level form of analytics.    

Artificial intelligence falls into a similar category, and is often confused with less advanced forms of rule-based automation. Of course, every company would want an intelligent machine to run its company’s operations, and do it more efficiently than any human could. But that reality seems to be some ways off. It’s much better to start or improve your use of standard business analytics tools to make smarter decisions. These insights will help your personnel to work more efficiently and focus on human interactions, or to find creative solutions to problems. Sure, it’s feasible that AI might one day be affordable to take people out of the equation. And, even then, these intelligent machines would still require readily available business rules to guide them. All the more reason to start by working on building the future-proof foundation of your BI and analytics operations.

Flexible data-integration

Organizations that have been inspired by the latest data trends are on the right track. After all, they see the potential of data and understand that they must do something with it. The most important lesson is that new technologies like big data, machine learning and artificial intelligence do not help in the short term. They are primarily a means to gather more data, even though the readily available data haven’t even been used optimally to drive performance. 

When organizations realize this, the need to build a robust business analytics foundation first becomes clear. This should be a central platform with which they can integrate and manage their existing business data in a flexible manner. Then they can analyze all the siloed data as a whole and develop algorithms that really help their business. With such a platform, they are also better prepared to eventually deal with the huge volumes of big data and IoT. But before that happens, an organization must first embrace the philosophy that a standard, future-proof analytics infrastructure is required. This will provide useful insights immediately and act as a bridge to future applications. 

Herbert Ochtman is the co-founder and EVP of Business Development at Pyramid Analytics. 

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