Overcoming the data challenge

Oftentimes, organizations get lost in their data and don’t know how to move forward. They might spend a lot of time and money looking for the ‘right’ analytics platform to solve all their business problems at once, as opposed to adding value and finding quick wins by quickly resolving a specific business problem.

A different approach to large-scale, multi-year data programs is to take a business-first perspective and then look at how the data can help. Traditionally, all data has been treated in the same way. But not all data is equal. Data is complex, it’s constantly growing, and it has mixed value and quality.

By starting with your problem-statement, outlining and testing assumptions, then working backwards to your data strategy - your organization will start seeing the value of transformation faster.

Starting with small, achievable objectives and focusing on a business problem—rather than on finding the right analytics platform—can add significant value to the organization and help shape long-term objectives. When you start moving into more advanced analytics with AI (Artificial Intelligence), it becomes even more important to start small and build credibility to achieve value.

What we see with organizations is that businesses run out of budget before they get to the final mile – and the final mile is where you get the value. Our approach is to tackle the final mile first and implement a strategy that tackles small incremental problems as you work toward the more advanced analytics and technology platforms.

By making small, incremental changes, you can test hypotheses and solutions quickly. In this framework, you’re reducing the cost of failure by failing fast. This iterative approach is important to being able to “turn off” tactical solutions and “turn on” strategic solutions.

In addition to sub-optimal data, other top challenges faced by organizations are a lack of skilled talent to carry out key technology roles and a risk-averse corporate culture that is slow to embrace digital change and disruption.

While there is a global talent shortage, organizations are also struggling to find the right talent for data transformation projects because they are still hiring the way they did 10 years ago—yet the hiring profile has changed.

Many businesses gravitate toward data scientists to run their analytics programs. But data scientists are trained to solve the ‘data’ problem rather than the ‘business’ problem. While data scientists have a valuable role to play, they should be part of a broader multidisciplinary team rather than the central driving force behind an analytics program.

A different approach is to seek out multidisciplinary individuals who understand how to solve business problems but also have an aptitude for technology. For example, an actuary may understand the financial cost of specific risks to the business, but an actuary who also has an aptitude for technology could help to frame a data solution around that business problem—which can then be handed over to a technology specialist to harden the architecture.

It is not always easy to find these multidisciplinary individuals, but organizations can also develop skill sets in-house, which can also help with talent retention. These individuals should be comfortable with ambiguity and be able to solve problems or build solutions through an iterative approach.

An agile work environment embraces failure. It means failing fast, so you can try again and make iterative changes to get the result you want. It’s where people can share their work, get feedback, and adjust, but they need to feel comfortable exposing flaws.

A data program requires a hybrid approach between agile and waterfall methodologies. An agile environment can help to articulate the problem, do the data discovery, and build out the solution, but it then must go through a series of tests—including bias testing—before going live.

This hybrid approach requires working in smaller teams, with people who have multidisciplinary skill sets—and a solid grasp of both technology and the business. It also requires structure, rigour, and governance around data, as well as support further up the chain to ensure the data is fit for purpose.

Our approach to creating cultural shift needs to remain constant. It is not just something that happens during kick-offs. It needs to happen within teams itself. We have found that finding champions within each team ensures there is a constant challenge of the status quo.

Data is the foundation for analytics. An analytics platform provides insights from data, whether for simple analysis, such as helping users visualize their data, or more complex AI applications. Without governance, it is easy to misinterpret the output of analytical platforms and therefore make poor decisions. Lack of governance also makes it easier to introduce bias into analysis.

While analytics are meant to replace gut-based decisions with data-driven ones, decisions based on poor data can be even more damaging, since you do not know whether that data is accurate or biased. Analytics is only as good as the data that underpins it. Users should understand why they made a decision, not because a tool told them to make that decision. Governance around how the data is interpreted inside the engine is important to the analytics outcome. This includes bias testing, auditability, and the ability to explain what the output is being used for and if that analysis is appropriate for that objective.