Why analytics is a dead end

With the advancement and widespread use of the Internet, there has been an astronomical increase in both the number of data sources and the amount of data generated from each source.

While this has helped administrators and executives of global corporations to gain higher visibility of their operations and systems, it has also created a huge gap in the data generated and directly used for decision-making and execution.

This gap results from the lack of sufficiently capable machinery that could translate the analytical results to executable decisions for improving operations.  

Let’s understand this phenomenon in detail.

The role of analytics in decision-making and the execution gap

Enterprise Systems of all kinds, digital twins of physical assets (IoT), and allied technology all generate massive amounts of data that are useless by themselves. Only when this data is passed through an analytics application and processed to give readable, useful information can a stakeholder make decisions based on them.  

A McKinsey report states that 61% of the interviewed respondents appreciated the change data analytics brought to their core business operations. Yet, their companies did little to respond to these results or build an effective, long-term plan to harness the power of data analytics. Since 2018 when the report was issued, things have changed: most larger companies now have analytic teams building data lakes, reports, and dashboards. Nevertheless, the “struggle to embed analytics in everyday decision-making” remains. Why?

Even analyzed data could be difficult to interpret due to the sheer volume and complexity. A human brain cannot compute these and make the right decisions to transform raw numerical results into executable actions. As a result, even though the gap between raw data (from the source) and processed information has been bridged, the actionable decision that can make such information get executed is still gaping.

But even more important is the right approach. The approach to analytics that is needed requires key performance indicators (KPIs) that allow to change business objects (leading indicators) and not only to focus on the final result (lagging indicators).

How can you bridge the analytics-to-execution gap?

According to the MIT Sloan Management Review, the biggest problem faced by businesses was the inability to translate data analytics into actionable data that the business can apply to improve its operations. This gap between analytics and execution requires some kind of software machinery that could translate KPIs and leading indicators into the respective required action.

For example, a supply chain manager may know that the availability of a specific product is low and future customer orders are at risk. Still, they may not know what to do with this information or what they can change to raise the availability in their business process.

The only way to bridge this gap would be to have some kind of application that could help you with the possible actions to get certain measurable results. This is where the need for an execution management system comes in.

Benefits of evidence-based decision making

Another important factor is leveraging and linking the provided actionable insights for decision-making by connecting them digitally to the execution. Evidence-based decision-making can greatly benefit a company in a variety of ways. Some of these are mentioned below:

• Having a 360-degree view of your business operations and understanding what each component means can help you make highly informed decisions.
• You will be able to take a more proactive stance in your business. When you leverage data analytics, you automatically shift from a reactionary decision-making process to a proactive one.
• You will be able to save a lot of time and resources. Data-driven decision-making allows you to make timely and accurate decisions, which protects you from the vagaries of the market and the expenses they bring.
• You will bring much more definiteness and transparency in the decision-making process when it is based on data evidence.
• Data-based decision-making will make scalability in all dimensions smooth and easy.
  

How can knowledge library creation and knowledge transfer help support decision-making?

Knowledge is dynamic and interdependent. Maintaining a knowledge base and encouraging sharing among peers allows your team to leverage past instances, capture decisions, and compare them with present scenarios to provide multi-layered actionable information.

It also helps you determine more accurately what works and what doesn’t, making you less dependent on the knowledge base or expertise of a single resource.

How to leverage execution management to help leaders figure out the best solution?

Execution management systems translate insights into actions. Being fully digital in decision-making and execution can provide much more transparency and consistency than we experience in current, especially tactical management processes. Thus, it augments the capabilities of leaders, allowing far better decisions and swift execution based on real-world data and forecasts.

Moreover, an execution management system can give more timely and accurate results. It can help leaders track down functions from the KPI to the atomic levels of the processes without any hiccups.

Incorporating data analytics into execution management has become a necessity for the corporate world. It offers great benefits for leveraging the existing analytics and decision-support systems to handle your business operations better promptly as you should. Through capturing relevant data and the resulting decisions, evidence-based decision-making and execution lead to faster and more effective results. Further and more intelligent automation is looming around the corner based on digitized decision and execution data.