Like many buzz word phrases, “digital transformation” has become a catchall term that means different things to different people. That’s an issue. Digital transformation is essential for companies to not only compete but survive.
Why data, AI, and analytics matter?
A recent survey of directors, CEOs, and senior executives found that digital transformation (DT) risk is their #1 concern in 2019. Yet of the $1.3 trillion that was spent on digital transformations last year, it was estimated that $900 billion went to waste - ouch! So why do some digital transformation efforts succeed and others fail?
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 oceans of data, with no growth or strategy in using this information to inform insights and planning.
Here are a few core principles data driven organizations are following. These principles are traits and behaviours thriving organizations are following.
Decide and Plan what to achieve
Often organizations are anxious to start using all the data insight they collect and it's easy to get distracted with all this shiny new data. They have gone through the process of ensuring the right data is captured, so the next logical step is leveraging it. Companies are measuring everything, and since data is abundant, businesses end up with hundreds of analytics projects designed to measure or describe each cog and widget 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 needed 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.
Understanding the gaps and issues
Along the way, organizations will run into data gaps and quality issues (garbage in and garbage out). What is meant by data gaps and data quality deficiencies? This might occur when you have multiple manual processes for a task, or maybe you want to measure something less obvious.
When gaps and quality issues are revealed, data-led organizations use this as an opportunity to streamline processes. This can include going back to the source systems and forcing more stringent requirements on the inputs of the data or the lens to which you're viewing the data. It could also mean building new systems to capture data or defining more explicitly the transformational and rationalizing steps needed before the data becomes useful.
Data Governance
Once the components for understanding have been put in place, data driven organizations take the time to determine roles and assign ownership or simply put, establish the Who. A lack of roles and ownership results in scenarios which no one knows where the truth lies - the data becomes arbitrary. Data insight driven organizations identify the different roles and most importantly, assign each one ownership.
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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 strategies. Having the right data element is critical in making sure the data collected has insight for decision-making. Here are a few best practices:
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. You will get biased false negatives and narrow insights if you're not measuring all factors of the business. Measurement is the key to understanding user behaviour, 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:
Companies are dumping billions into “digital transformation” initiatives — but 70% of those fail to pay off. That’s because companies put the cart before the horse, having a narrow focus on specific technology (“we need an AI strategy!”) rather than doing the heavy lift of fitting the change into the overall business strategy first. You don't want to just measure and see what the data tells you. You want to know the questions first, understandable if you don't know what you don't know but when adopting a data strategy there needs to be some questions aligned with goals and initiatives. Not only should they align tech investments with business goals — they should also leverage internal and outside resources, develop deep understanding of how changes will affect customer experience, and use process techniques borrowed from the tech world (experimentation, prototyping, etc.) to facilitate change.
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 everyone wanting more and backing that up with the knowledge (and data) of how to deliver. Finally, in the maintenance stage, where iteration is an ethos of every project, attention is paid to measuring uptake and managing platforms, champions built, and every victory celebrated.
