These days, companies can no longer remain competitive just because they have innovative products or services. They need the right data to make real-time, informed choices on the entire lifecycle. In this way, any business is only as good as its data.
The trick is transforming that raw data into analytics – to provide decision-makers with actionable knowledge to enhance workflows, boost marketing and sales programs, and understand customer behaviors.
But to get the most out of analytics, it’s critical to avoid common pitfalls.
In many instances, leaders can become so captivated with analytics that they want every insight delivered in real-time. This cannot only be a waste of money, it can also come back to bite you. For instance, you may want to stay clear of putting your revenue report into an analytics engine. That’s because if you experience canceled orders on a regular basis, the sales team may in end up in a chaotic mindset: At 10am they’ve reached their goals, but at 3pm the updated reports indicate they’ve fallen behind. Because of scenarios like this, you need to understand which analytics deserve immediate action, and which can wait.
On a related note, your ability to act on specific data can depend on the strength of your infrastructure. Leader can become disillusioned when they expect actionable insights but the infrastructure’s performance can’t support those expectations. To make your analytics initiative a success, your organization’s core architecture must be able to support real-time data manipulation, ingestion, and processing.
Don’t forget to identify the types of dashboards users will need. This ensures that the analytics will be delivered in the right formats, in a way that solves actual challenges for those employees. By putting the user at the center of the analytics initiative, you don’t disrupt their workflow and align the solution with how they perform their jobs each day. You get an analytics system that is uniquely targeted to individual user needs.
Another key tip: Be sure to combine real-time data with historic data. That’s because the value of real-time data increases exponentially when mixed with historic data. By doing so, users can compare and contrast insights at that very moment. This ability can be extremely valuable in many common business scenarios. Picture, for example, analyzing temperature data transmitted by a sensor inside a machine. While understanding such data in real time indicates if the machine is operating efficiently, historic data allows you to gain even a richer understanding of the machine’s performance.
You must also make sure that the analytics initiative can account for internal data as well as contextual data. To clarify, this contextual data could be related to competition, markets, and customer segments to give you a comprehensive set of facts and trends. Ideally, there should also be a direct link to the business technology plan and long-term financial plan. With access to real-time and contextual data, you can transform how the company makes key strategic decisions. You become more data-driven, and make choices based on facts and trends, not on so-called “business instinct”.
Despite all of this advice, you can’t successfully execute if you don’t have the right team. In this case, you need people with a deep understanding of how specific business units function, and how they are all inter-related. In short, your analytics team must act as consultative partners, not as employees who merely execute the strategy. And to ensure long-term success, provide them with as much business knowledge and relevant intelligence as possible. The more they have, the better they will be at providing analytics that improve the bottom line.