By now, you’re probably more than aware of the big data explosion that’s occurring throughout the business world. Thanks to the billions of connected devices that have emerged over the past decade and the staggering amount of data they produce, companies are investing heavily in this field.

In fact, International Data Corporation says that the market for big data will reach a whopping $48.6 billion in 2019. However, in the face of this incredible growth, Gartner predicts that 60 percent of big data projects will fail or be abandoned over the next year.

CEOs and CXOs across the globe continue to scratch their heads, searching for ways to get the most value from their investments.

Enter the Cognitive Data Product

The solution to these woes begins with a mindset shift: You should no longer view data as a one-time investment. Instead, see it as something that can be operationalized into a scalable and repeatable long-term process.

With most out-of-the-box big data solutions, companies use descriptive, predictive, or prescriptive insights to generate a one-time report on the data at hand. While this can help with troubleshooting, launching promotions, or making recommendations for individual products, these data solutions are not reusable or continuous, making this a short-term and shortsighted investment.

Cognitive data products, on the other hand, utilize algorithms to automatically infuse data into your business workflows in a repeatable and scalable fashion. This approach frees up your data scientists from otherwise time-consuming processing requirements for building models, empowering them to propel the company’s big data strategy and focus on the future.

Embracing Cognitive Solutions

As with every new endeavor in the world of big data, cognitive data products come with a set of challenges that each company should handle according to its unique needs.

First, building data products requires a great collaborative effort from multiple stakeholders, ranging from analysts and platform engineers to data scientists. Given the dearth of data scientists the tech world is currently experiencing, it’s important that your organization sets aside time to follow through on this collaborative effort.

Next, as the world experiences a deluge of data from every possible direction, most companies organize under the mantra “Every department for itself!” Rather than continue the independently siloed analysis of such data, companies need to develop a common platform that integrates these sources.

Finally, making the process repeatable and scalable can be quite challenging. It’s important that your company increases the ease of access for the right stakeholders in the process to facilitate the continuous repeatability and scalability of insights.

Companies that organize with these three factors in mind see the best results from their cognitive data products. Here are five additional steps that will make the process even easier:

  1. Think strategically. Don’t make ad hoc queries such as “Why was there a 5 percent increase in customer churn?” This narrow, one-time approach won’t help you build a repeatable, scalable solution.Instead, make broader, more strategic, and more actionable queries: “Predict my monthly customer churn, identify the factors leading to the churn, and list the preventive actions I can take.”
  2. Identify the key players. Great products are rarely built by a single person or a single group, and the same principle applies to cognitive data products. For any given product, you might need the support and collaboration of business analysts with domain expertise, data scientists with algorithm expertise, and data engineers with platform and scalability expertise.Identify the multiple functions that must come together, and provide them with a platform that allows for such collaboration.
  3. Select a collaborative platform. Most business intelligence, discovery, and machine learning APIs solve pieces of data product problems, and they often need to be assembled manually. This can be highly cumbersome; it’s far from ideal.Instead, look for a single platform that grants you the ability to build the entire data product from end to end, avoiding a piecemeal approach.
  4. Focus on automation. A setup that requires manual execution at multiple points throughout the workflow isn’t a data product. For a product to be truly effective, it should be predominantly automated and require minimal human intervention.This is where selecting the right platform that has automation baked in is essential. Leveraging cognitive computing to build data products provides a tremendous boost to speed.
  5. Prioritize usability. A data product is only useful if anyone and everyone in your organization can access and understand it. If there’s a significant learning curve, it will likely fail.The cherry on top is being able to embed the data product’s output into your existing software, whether it’s your CRM, ERP, CMS, or marketing automation tools. This will allow teams to quickly and easily turn insights into action.

Stop working so hard for single-life reports and data outputs. Instead, create a cognitive data product that automatically extracts insights and allows you to make smart, future-minded business decisions.

It will take time, energy, and expertise — but the payoff will be tremendous. You will run a company that’s prepared for both today and tomorrow.