We are now deep into the big data revolution, and the majority of organizations have at least recognized that they need data in order to become intelligent. For some, this means buying the next flashy software someone tries to flog them, hiring a data scientist, and leaving the two to get on with it. Put your feet up, fire up the fondue set, and wait for the insights to come pouring in.
Companies that do this - while admittedly likely doing better than those that do nothing - will fail. To use data to its full potential, decision making across the organization needs to be based on data as opposed to gut instinct. A recent study by MIT Sloan Management Review and SAS ‘The Analytics Mandate’ concluded that an ‘analytics culture’ is the driving factor in achieving competitive advantage from data. David Kiron, executive editor for MIT Sloan Management Review, noted: ’We found that in companies with a strong analytics culture, decision-making norms include the use of analytics, even if the results challenge views held by senior management. This differentiates those companies from others, where often management experience overrides insights from data.’
For organizations to instil a data-driven culture, they need to evolve from merely reporting data to inspiring and invoking action. Data must be easily accessed and understood, and organizations must ensure employees have the knowledge to ask the right questions, and the appreciation of its importance to use it.
There are, however, a number of challenges to instilling a data driven culture. A NewVantage Partners’ survey found that over 85% of respondents report that their firms have programs in place to build data-driven cultures, yet just 37.7% report success thus far. Top of the list is getting buy-in from senior management. In a recent survey of 2,165 data professionals commissioned by KPMG and conducted by Forrester Consulting, 49% of respondents said their C-level executives don't fully support their organizations' data and analytics strategies.
We spoke to 6 experienced data professionals from some of the biggest names in the industry about why it is so important to create a data-driven culture and how best to do it.
Anyone looking to implement a data-driven culture needs to think really deeply about what problems they’re trying to solve and what questions they’re trying to answer. If a company doesn’t give sufficient thought to the specific goals they’re trying to accomplish, then they’re going to fail with analytics. You can’t just hire some data scientists and hope that they come up with great insight. The data scientists typically don’t know the business, and the business folks often don’t know the data science. They need to work well together, which means that they both have to know what they’re trying to answer and why.
Being data-informed is great, but being 100% data-driven is a bit dangerous depending on how you define data-driven. For example, if you’re a gaming company and you’re selling in-app purchases. If your goal is basically revenue, launching a prompt that prompts your users every ten seconds upselling in-app purchases is great for short term revenue gains, but perhaps not for long term gains because you will eventually lose these users. So it’s important to look at the design and consider the overall strategy of the product.
How do you structure a team and give it purpose? Setting a metric goal will give it focus. Set metric goals like monthly active users (MAU) or daily active users (DAU) - metrics that are easy to understand and don’t require a PhD. Remember that you are communicating these upwards, to investors, and to other product teams as well.
Creating a culture of accountability is also important. When metrics drop or increase, call people out. Give them props for increasing a metric and, without scolding them, make people realise when they make a mistake and the metric drops. A key component here is reviewing your metrics in a weekly meeting.