The role of leadership and inspiration

Don't let leadership challenges undermine your data strategy

We’ve said it before – raw data has no value, no matter how much your organization is sitting on, if you don’t have the right frameworks in place. In that case, is it any wonder that the industry often refers to large volumes of untapped data as data lakes, oceans, seas – where it’s possible to drown? The key to whether your data strategy sinks or swims is deceptively simple, but definitely not easy.

Leadership is your data-driven compass and engine

Plenty of companies invest in the right tools and the right people to put data to work, but fail to achieve results. Despite having all the right things in place, a key aspect of data strategy is missing: the ability to implement sweeping changes that impact all levels of the company..

That takes authority, and the C-level is the only group of people that have enough of it. Your company can be as smart as it’s possible to be, but if the organizational structure never evolves, you’ll never reach the point of being truly data driven.

In many cases, we’ve seen that simple things like data ownership is fraught with whirlpools of politics and silos created out of overprotectiveness and even misguided competitiveness. If product owners hoard their data and use it only to further their own goals, you miss out on the huge transformational effects that it can have.

The power of inspiration can never be overstated

If you’re a leader of a large company and you aren’t aware of what’s possible with AI, you’ll never be able to inspire, lead and make decisions that move the company toward data-driven successes.

In order to implement the changes needed to develop and create an effective data strategy, the leadership team needs ‘lead’ what’s going on in the field– and creativity and motivation are essential here.

Company leaders also have to forge partnerships and foster collaboration to make fundamental changes and big leaps forward. This requires executive decision-making abilities as well as plenty of out-of-the-box thinking.

Take future-focused banks, for example. They provide tools within their apps that make it simple to book movie tickets or pay for parking or even taxis, because they have established innovative partnerships with other service providers behind the scenes. This data-driven creativity solves customer pain points even while growing the shared distribution network, leading to gains for every party involved.

Measure, follow up, communicate, repeat

We’ve discussed leadership authority and creativity as essential engines for data-driven transformation. The third engine is a robust monitoring process. If you don’t know where you are, how can you ever determine whether your organization has taken actual steps toward where you want to be? There’s honestly nothing less inspiring than drifting around aimlessly on your data lake.

Define precisely what KPIs you seek to achieve, and then put a system in place to determine that your changes are leading to an increasing number of data-driven decisions. Also important here is identifying which initiatives aren’t leading to results. As resource-consuming investments, it is essential to shut them down as quickly as possible if they aren’t leading you toward your goals.

Building ethical - and acurate - machine-learning models

On a much broader level than organizational change, the fourth driver of data-driven transformation is governance. Ethics is becoming an increasingly important topic with every day.

This is a very active concern for our clients. In HR, for example, imagine building a machine-learning engine that matches jobs with candidates. In order to train that machine, you must feed it historical data. However, gender can no longer be factored into this process, according to law. If you train the model using this past data, do you include gender? Will you even be able to achieve an accurate model if you don’t take all of the data into account?

Data ethics is very important when it comes to people-focused processes, and it cannot be ignored as you move forward on your data strategy roadmap.