Top 5 questions business leaders are afraid to ask about Big Data


Technological disruption is transforming how businesses function. More and more companies around the world are starting to integrate data and technology into their business practices to improve efficiency and, most importantly, to stay ahead of the competition. As the market is getting more competitive, companies are looking for intelligent ways to make the most of their vast amounts of data. Using Big Data properly allows companies to access real-time data for better decision making to establish more targeted talent recruitment and retention programs, conduct better internal and external communication, and design optimized machine maintenance schedules. In short, data-driven systems, especially when artificial intelligence (AI) and machine learning are applied, enable business leaders to make quicker and more informed decisions.

Despite the benefits offered by Big Data, many business leaders are hesitant to adopt it because they are still confident with the same old blueprint that worked for them in the past. However, the business landscape is changing rapidly, and business strategies need to adjust accordingly. Moreover, business leaders may be hesitant to ask the right questions, because they are afraid of being judged for not knowing the answers already. The reality is, it’s normal to have knowledge gaps, and it’s much better to ask for clarification when in doubt to ensure that everyone is on the same page. With that being said, it is up to you to ask the right questions to guide your company forward.

In that spirit, this article is meant to give you a starting point, with answers to the top 5 questions that business leaders are afraid to ask about Big Data. I normally have this discussion around applying AI to human resources (HR), as well as manufacturing optimization, though the questions apply across all business functions.


5. How do we start collecting data now? Do I need a new system? What about the old one, what do we do with it?

The good news is that companies already have a good deal of data in their current systems. Whether it’s the existing HRM system, or CRM database, or even paper-based sign-in sheets to confirm employee attendance, companies have more data than they realize. However, they often need help to identify, categorize and move this data to a central data system – we generally recommend using a public cloud storage system, as it is cheaper than establishing your own, and it allows for advanced data tools like machine learning to be applied.

4. How can I be sure that investing in a data system will have ROI?

It’s very hard for a data system itself to have ROI. Rather, companies must first understand what their business needs are and work backwards. We generally work with businesses to identify cost drivers, and then determine what aspects of those cost drivers can benefit from centralized data, then work to get good data visibility through a management dashboard. Once the data is ready to go and has good visibility, we can then start to gain better insights into the data and use it to solve certain business problems.


3. How can we centralize our data?

Many companies are looking to centralize their data, and the main options are a private or public cloud, or some combination of the two, depending on your business needs. Although we usually recommend a public cloud, whatever you choose, the key is not to move all your data at once in some kind of mass migration, which is costly, time consuming, and highly disruptive. Rather, just move what you need to solve a particular problem and build from there. This allows for a natural progression of data migration saving time and effort. Too many companies conducting mass migrations have been ill prepared for the amount of effort necessary to make use of the data once centralized.


2. What kind of questions can AI and machine learning answer?

AI and machine learning are the great leap forward that can help businesses make better decisions. Most businesses are not yet ready for these types of data science projects, and it is our goal to help them get there. Once you have the necessary data collected and good visibility, you can start using the data. For human resources, this could be answering key questions, such as “What kind of person is most successful as a supervisor,” or “Who is most likely to leave in the next three months.” For manufacturing, it could be setting up IoT for data collection at the machine level, or a complex optimization of the production line using machine learning. With the right data and tools, it is possible to reduce human bias through AI, allowing business leaders to make better decisions.


1. How will I know what the data means?

Understanding what actionable insights your data can tell you is no easy task, especially if the organization’s functional teams have not done it before, or have limited data experience. Generally, a close collaboration between the functional business teams and the internal or external data consultants is necessary to gain the best insights. The functional teams know the business and can work with the data teams to ask and answer interesting questions about the findings. Over time, the functional teams can develop deep skills in this area and learn to do it on their own.

The talent strategy of the future

The ability to access important information in real-time is the key to effectively designing the right talent strategy for your business. Adopting the right technology now will enable your company to thrive in today’s highly competitive market. The AMPOS team can show you the way forward.

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