What is a data-driven culture?
In a data-driven culture, people use accurate data sets to inform critical organizational decisions. They use the evidence of metrics and analytics to generate useful information that helps guide better decision-making.
Organizations may do this at the executive level or aim for all levels. It depends on many factors, including their state of digital modernization, business goals, available budgets and expertise, and the need to scale data analytics services to address specific problems.
Huge demand for analytics
The data analytics market is booming, with businesses spending an estimated $215 billion in 2021 on big data and business analytics solutions.
Gartner notes this year’s top data and analytics trends relate to three themes:
- Activating diversity and dynamism using adaptive AI systems.
- Augmenting people and decisions to deliver enriched, context-driven analytics created from modular components by the business.
- Institutionalizing trust to gain value from data and analytics at scale. This involves managing AI risk and ensuring connected governance across distributed systems.
Here’s what you need to ramp up your data game
Although most organizations today place a high value on data and analytics, they’re at different stages of this journey.
Here are some things to consider when trying to build a data-savvy culture.
1. Define your business goal for analytics.
Know your business, talk to your managers, and identify clear, relevant goals for analytics. The goals will shape what metrics you need to capture, and what systems you need to integrate to make that happen. You can start small—with one achievable project that shows the value and builds trust and support for other initiatives.
2. Get the right stakeholders involved.
CHROs and business leaders are important allies. They get you access to the right stakeholders, the right business problems you can solve using analytics, and enable access to resources for analytics projects.
3. Establish data governance.
A data-driven culture uses data governance and master data management (MDM) policies to keep data accurate, usable, and secure. There’s one central source of data truth built on well-planned data integration.
Also, people are clear about what metrics they handle, and how those metrics move their key performance indicators.
If your organization is considering AI, manage it with care, too, to avoid introducing program bias into decision-making algorithms. You’ll also need to protect personal privacy and guard against data breaches.
4. Spread data literacy.
This starts at the top. Train leaders to understand the true value of data and the language of the technology landscape.
Also, upskill your workforce. Not everyone needs to be a data analyst or a high-level coder. They just need enough data skills to perform their jobs effectively and efficiently. Establish a baseline of data literacy for each job or work area—marketing, finance, HR, sales, and so on.
Then provide access to training and ongoing learning experiences to develop your people’s data skills.
5. Use data storytelling.
Poor communication may limit or prevent employees from translating data insights into meaningful change. Data storytelling involves creating engaging narratives to communicate data insights. Visualization and infographics can help illustrate messages for decision-making. Also, be willing to always explain your analytical choices to your people—transparency builds trust.
6. Consider context-rich analysis.
Gartner predicts by 2025, context-driven analytics and AI models will replace 60% of existing models built on traditional data. Contextual analytics enables a deeper merger of analytics and business applications to make our data more dynamic and personalized for end-users. You’d be able to click on an element to gain instant context and relevant metrics, without having to switch windows or applications. In the past, traditional Business Intelligence analytics did not do this. But it’s the wave of the future.
7. Avoid the pitfalls.
- Executive power-hoarding. Some executives don’t want to relinquish the power to run their organizations by fiat. They may prefer decision-making based on personal, family, political, or other reasons. They don’t care about the data. The business will suffer.
- Data access problems. Before you start an analytics project, fix your basic data access issues first.
- Not collecting the right data. Perhaps you already collect tons of data from all your systems and believe this makes you “data-driven.” It does not. Defining the problem or business goal should precede and inform how you go about collecting the right data.
- Amateur hour. You need a core of skilled experts at different levels. Don’t dabble in analytics only to regret your mistakes later—mistakes cost money. Be professional and hire people with good statistical skills.
- Dazzled by data. Don’t get lost in creating amazing predictive algorithms, elaborate dashboards that don’t deliver metrics people use, or disappear down other data rabbit holes. Define real business problems you need to solve with analytics help.
- Siloed analytics. It’s good to use analytics to help employees and customers. Have an open mind on how analytics can help different users.
Building a data-driven culture takes time, but it’s worth it. Data analysis can help you cut costs, improve process efficiencies, detect anomalies, and understand your workforce better.
Pixentia is a full-service technology company dedicated to helping clients solve business problems, improve the capability of their people, and achieve better results.