3 Questions Every Data-Driven Company Needs to Ask
As the chief marketing officer of a data integration and analytics company, I spend a lot of time discussing the hidden possibilities in data. Being “data-driven” is something more and more companies strive to become, especially in these times of uncertainty.
One can easily get caught up in the notion that being data-driven is really about machines. The reality is, to make substantive gains, decision-makers must embrace the human side of the equation as well.
We’re all human, and we all have that never-ending belief in our “gut” — that overwhelming feeling that we just know the right answer, whether it’s from experience, hearsay, or a collection of recent observations.
The truth is that it’s really the combination of human intuition with analysis that unlocks the value of data. So while business intelligence (BI) and visual analytics deliver logic, computation, and speed, humans have an incredible ability to bring vital creative thinking to the mix. If your company has implemented these technologies with limited effect on the bottom line, then you may be forgetting the human factor.
Here are three questions that you and your leadership team should be asking to get the biggest impact from your data and analytics efforts.
3 Questions Every Data-Driven Company Needs to Ask
- Do our people have the necessary skills to leverage data and analytics tools?
- How can we mainstream analytics?
- Do we have the data governance needed to thrive?
1. Do Our People Have the Necessary Skills to Leverage Data and Analytics Tools?
Machines tend to work linearly, whereas humans tend to think in a myriad of directions almost simultaneously. We might see something and immediately think of something else, asking the next question, and the next — and that’s what often leads to breakthroughs.
If we humans aren’t the ones asking the questions of the data — or we don’t know how to ask — then only the obvious insights are uncovered by our tools. Even as we augment our analysis with technology like the most powerful BI platforms, for instance, they still rely on human guidance to maximize their value.
In research commissioned by Qlik to inform our first Data Literacy Index, we found that out of over 600 global enterprise decision-makers we surveyed, just 24 percent are confident in their ability to make decisions using data. That means the vast majority of people within the average business are likely not maximizing the value of new technologies and the data they create. In a world that is increasingly based on the use of data to drive new revenue and support more effective decisions, that proficiency, commonly referred to as data literacy, is vital.
Leaders must invest in data literacy training for all their staff to ensure they have the skills necessary to effectively read and work with data, which will set the stage for increased data use and smarter decision-making. There are a variety of resources leaders can readily tap into to help close the data literacy skills gap, including assessments, free training courses, and certifications from the Data Literacy Project, a global community with the goal of creating a data-literate world.
2. How Can We Mainstream Analytics?
The exploration of data and data discovery is what fuels innovation. Data discovery is the enabling of non-technical users to access and explore data, ask questions about that data, and find insights within the data. Some would say it’s a machine-assisted process to help humans draw out the information they need. I would argue that it’s a human-assisted process that guides machine-driven technology towards answers.
Machine learning algorithms are great at culling through billions of rows of data across multiple data sets to show correlations, draw conclusions and demonstrate those visually. When humans apply their intuition and data literacy skills to the insights generated by these algorithms, it naturally leads to additional questions, which creates a virtuous cycle of more insights for every new question.
The value these insights generate can fuel the interest and willingness for employees to apply analytics to every part of their job, and to share those findings with their colleagues. And this is how analytics goes mainstream.
This is also why self-service analytics is becoming a major driver of the democratizing of data within a company. It aims to go beyond data and information being in the hands of a few experts, and instead sharing it with the subject matter experts throughout an organization. The result is the ability to empower everyone — regardless of title or department — to make decisions using relevant data.
Leaders need to make sure that collaboration and sharing of insights from subject matter experts to relevant parts of the company are built into the culture. By using data themselves when talking to various parts of the company, and holding up great examples of employees leveraging data for impact, leaders can set that tone.
3. Do We Have the Data Governance Needed to Thrive?
As leaders increase data availability, they need to remember to set parameters through governance based on roles and data type. Not all data is created equal or has equal value, and not everyone needs access to every single piece of data — only the data that is relevant to their roles or applicable to their analysis. For example, someone from marketing likely doesn’t need access to detailed accounting records, but rather only needs to understand the data associated with revenue related to specific campaigns.
To start, companies should look to technologies like data catalogs to help organize, structure, and transform the data that will have the most analytical value. Acting as a single source of truth, a data catalog serves as a governed repository where data access and use can be controlled and tracked by role, while also providing users access to new, trusted, and relevant data sources.
This gives leaders the confidence that regardless of department, everyone is exploring analytics-ready data that can confidently be used for decision-making, while also providing explorers secure access to additional trusted data sources to blend into their analysis.
Becoming Human-Driven, but Data-Informed
The next time you think about becoming data-driven, think along the lines of becoming human-driven, but data-informed. That’s why it is so important to invest in reskilling our workforces to become more comfortable with data analysis tools, to amplify how they’re thinking and exploring within their data universe. The combination of skilled humans and today’s modern analytics tools can change outcomes, re-invent industries, and unlock human potential.