Why data, AI, and analytics matter?

Over the past many years, business strategists have been obsessed with a data insight driven mindset. But why does data matter so much? Well, the answer is straightforward: Businesses want to make decisions objectively, remove biases and identify inefficiencies. Data can reveal our habits and what our next action might be. It opens the door of opportunity and brings to the front and center, what’s possible. This idea of leading brings the culture to collect and analyze data for decision-making.

According to McKinsey, 70% of digital transformation projects fail to meet the stated goals. It means most projects revolving around data are not getting the results they are looking for. More importantly, companies are flooded with mountains of data, with no growth in using this information to inform insights and strategies. There are organizations, though, getting it right.

Here are a few core principles data organizations are following. These principles are traits and behaviors thriving organizations are following.

Decide and Plan what to achieve

Often organizations are anxious to start using all the data insight they collect. They have gone through the process of ensuring the data is captured, so the next logical step is utilizing it. Companies are measuring everything, and since data is abundant, businesses end up with hundreds of analytics projects designed to measure or describe each facet of an organization.

If a business challenge (problem) is decided based on business value it can unlock, then it is easy to come up with the questions to which you need to get an answer. The knowledge of datasets can make it easier to choose what data can help in getting an answer to the question.

Data Governance

Once the components for understanding have been put in place, data insight driven organizations take the time to determine roles and assign ownership or simply put, establish the Who. A lack of roles and ownership leads to scenarios in which no one knows where the truth lies. Data insight driven organizations identify the different roles and most importantly, assign each one ownership.

Best Practices

Data-driven organizations see every new project as an opportunity to establish best practices, and to use it as a model for how the next project should be approached. Starting with best practices can accelerate future initiatives. Having the right data element is critical in making sure the data collected has insight for decision-making. Here are a few best practices:

• Set clearly defined goals.
• Enforce data collection from all sources identified.
• Ensure data is a central point for the organization not just to be used by one department.
• Inform and educate everyone on how to utilize data assets for decision-making.
• Collaboration among all departments and groups is essential.

Measuring utilization and adoption

As much as companies believe in measuring factors that affect businesses, the importance of measuring analytics usage and adoption also must be considered. Measurement is the key to understanding user behavior, everything from consumption to creation.

There are plenty of ways these aspects are measured, but these are the three main areas that need to be focused on.

User Engagement: The goal here is to figure out how well analytics are adopted and how much engagement is happening with users.

Utilization: The focus is on the insights rather than the platform itself. Here, the focus is on digging deeper to see what things people are looking at and what they are interested in learning and analyzing.

Performance: It is the balancing act of making sure end users have the experiences they want, getting what they need when they need it. Of course, uptime comes into play here, making sure there is an uninterruptible service, making sure when data is required to make decisions, that data is there. And finally, using normal monitoring techniques for reviewing logs, parsing out alerts from systems, and fixing any hardware failures.

Conclusion:

The principles shared here fall into three stages of an analytics strategy. First is the foundation – the place where the framework is set of what is being measured, where it comes from, how it is going to be rationalized, and who will own, manage and use it. Second is the execution phase, starting from how data-driven organizations use tactics that leave audiences wanting more and backing that up with the knowledge of how to deliver. Finally, in the maintenance stage, where iteration is a mantra of every project, attention is paid to measuring uptake and managing platforms, champions built, and every victory celebrated.