Roadmap to Becoming a Data-Driven Organization

/ October 9, 2020 | 5 Mins Read

Roadmap to Becoming a Data-Driven Organization

Exploding quantities of data have the potential to fuel a new era of fact-based innovation in corporations, backing up new ideas with solid evidence. If you’re looking to become a data-driven organization, get the basics right and help you build a solid data and analytics environment.

No longer the exclusive domain of tech giants and disruptive startups with plenty of investor cash to burn, data literacy has evolved into a basic competency for modern workers in every industry.

In 2020 and beyond, it’s data that separates the leaders from the laggards, and it’s those organizations that not only capture the most high-quality data but understand and act on insights in real-time. 

While most organizations understand the value of collecting data and have already invested in tech stacks that track website analytics, campaign performance, and revenue, few qualify as truly “data-driven.” 

In this article, we’ll lay out a roadmap for becoming a data-driven organization--a quick spoiler: it has more to do with leadership and culture than technology. 

1. What Does it Mean to Be “Data-Driven?”

“Data literacy” narrates the ability to interpret and take action on data in context. 

That means, workers must have an understanding of where data is coming from and how it’s structured, which analytical methods and tools provide the right information, and how to probe that information and determine the best course of action.

A data driven-organization uses data to inform strategic decision-making and drive action, allowing them to carve out a competitive advantage, respond to changes in the market, and better serve their consumers.

2. Identify Specific Goals for Your Data Strategy 

The first step toward becoming “data-driven” is evaluating your organization’s core business objectives and identifying the specific use cases that move the needle on those big-picture goals.

Make sure you can answer the following questions before investing in any tools or introducing new processes:

  • What problem do you hope to solve?
  • How will resolving this issue impact your organization?
  • What opportunities are you targeting?
  • Does pursuing those opportunities make sense in the context of your business strategy?
  • What is the desired outcome?
  • How will you measure success?

From there, you’ll need to determine what data you currently have, whether it provides the information needed to achieve the results that you’re looking for, and how to reach those targets.

Here, data science, IT, and leadership must work together to determine which strategies will yield the best outcome.

Keep in mind, if you’re targeting multiple goals, you’ll want to make sure you tackle each use case individually. A big data project designed to improve CX is going to look much different than one tackling real-time threat detection.

Data plays an essential role in any organisation. Ability to understand the data & its application can make or break your business. Oftentimes migrating data from one source to another can be a big challenge, says Gabi from Trujay who expertise in CRM data migration.

3. Audit Your Current Data Strategy

Once you’ve identified your core business objectives, you’ll want to get a clear picture of where your data strategy is right now. 

  • Identify information gaps. Are you missing critical information? If so, find out if that information is siloed-off or you don’t own it at all. 
  • Determine if you’ll need to integrate additional data sources. Do you need to incorporate siloed information, 3rd-party insights, or publicly available data sets?

  • What capabilities will you need to achieve the desired outcome? Do you need access to real-time data streaming? Are you integrating legacy equipment? Building a distributed network of IoT devices? 
  • Who is the target end-user(s)? The tools you choose should align with the end-user’s skill set and role. Sales and marketing teams will have different priorities than the finance department or IT. 

4. Communicate the Business Impact of Prioritizing Data Literacy

Because creating a data-driven culture is a holistic, organization-wide initiative, it's the C-suite's responsibility for prioritizing data literacy and acting as an agent for change. Leadership must communicate the value of this initiative, using tangible examples to illustrate the potential business impact and overcome internal resistance to change. 

This includes:

  • Providing clarity around how tools help workers become better at their jobs. 
  • Explaining the cost of a failed deployment and what that means for the organization moving forward. 
  • Highlighting successful use cases/initiatives from other brands in the industry to make the benefits seem more "real." 
  • Including stakeholders at all levels in the process.
  • Offering specific examples of how using new tools/processes will enable more interesting work that adds more value to the organization. 

5. Democratize Access to Quality Data

Microstrategy’s 2020 Global State of Enterprise Analytics report revealed a significant divide between which roles come with data privileges and which are kept in the dark. 

In the US, just 44% of front-line workers have access to data and analytics, compared to 81% of management teams and 81% of executives. 

A total of 60% of “data-starved” front-line workers reported waiting hours or even days for the insights required to make an informed decision, and just 3% have the ability to arrive at a decision within seconds. 

The problem is, front-line workers are typically the employees that have the biggest impact on the customer experience--think sales reps, customer service & success teams, social media marketers, support desk staff. 

Unlocking access to consumer insights allows these employees to provide better service, personalized solutions, and use data to improve existing strategies. 

By making data accessible, brands give front-line employees the insights they need to directly influence customer retention, satisfaction, and loyalty, run more effective marketing campaigns, and close more deals--all critical to the organization’s bottom line. 

6. Provide the Right Tools

The Microstrategy report also found that part of the solution is investing in more intuitive analytics and business intelligence platforms that can help bridge the skills gap--think self-service platforms with embedded AI and machine learning capabilities that offer some extra support to workers and help them save time. 

For example, graphs, charts, and other visuals are on their way to replacing traditional dashboards, as they make it easier for users to quickly interpret the data and take action, while many self-serve BI tools use embedded AI and machine learning to recommend the best actions for any given situation.

With that in mind, it doesn’t make sense to leave platform selection to IT or the CIO. Make sure to include the end-user in the decision-making process and allow employees to test different options and select a solution that aligns with their skills, workflows, and use cases. 

Big-picture, each department’s BI, analytics, and data science tools should integrate into one cohesive system, but beyond that, the choice should fall on the users themselves.  

7. Make Training Approachable & Role-Specific

According to a survey by the Harvard Business Review, 93% of executive respondents cited people and processes as the main barriers to capturing actionable insights from internal data. 

Researchers found that many of those executives also reported that their firms were investing a significant amount of their budgets into new technologies while failing to address their organization’s relationship with data. 

Yes, tools are part of the equation, but without the right culture, strategy, and skills in place, organizations face an uphill battle when it comes to capturing the value of those investments. 

Make sure your data strategy makes continuous training a priority and helps workers develop a contextual understanding for finding the right information and using it to guide the decision-making process.

Putting training into context will help you achieve buy-in, create a shared understanding of this initiative, and help improve job performance and customer experience at every level.  

Final Thoughts

Becoming a data-driven organization is a challenging, often overwhelming endeavor that doesn’t happen overnight. 

It’s a long-term strategy that requires planning, patience, and an internal champion ready to change mindsets and fight resistance at all levels. 

Sam Makad is a business consultant. He helps small & medium enterprises to grow their businesses and overall ROI. You can follow Sam on Twitter, Facebook, and Linkedin.

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