Companies across industries have come to embrace Big Data. But for many organizations, enthusiasm hasn’t translated into success. It’s one thing to set up processes to capture details about every facet of the enterprise. It’s quite another to develop an ecosystem that taps into that raw information and facilitates it in a way the entire workplace can benefit from.
The reality is that most organizations have low analytics maturity rates despite significant investments in time and money. What’s holding these businesses back from turning the corner with their data?
Here are the three biggest roadblocks.
Low Data Literacy in the Workplace
No amount of quality data can make up for a workplace unable to communicate about data effectively. This is the state of an enterprise that has laid a digital foundation but failed to promote data literacy. It’s no longer enough for data professionals to understand data’s value; every employee in the company needs to buy in. Doing so improves decision-making, collaboration, and innovative thinking.
Establishing a culture fluent in data isn’t easy, though. Leadership and data teams are paramount to instilling the importance of data into the rest of the company. New roles like the Chief Data Officer and Data Evangelist, as well as data architects, scientists, engineers, and analysts, must also do their part to help translate technical concepts to the rest of the company. This process won’t be quick, but it’s a prerequisite for getting the most out of any analytics tools, as well as ensuring that individual employees can interpret findings by themselves.
Storing Unstructured Data Without a Plan to Democratize It
As mentioned at the top of this article, companies don’t need convincing when it comes to collecting data. But just as it’s easier to buy supplies for a project than it is to use those supplies to produce a desired outcome, organizations often have an ‘uh-oh’ moment when they look at their various data volumes.
Rest assured, best practices exist to cleanse unstructured data into a state where it can be mined for insights. According to data modeling software company, ThoughtSpot, these steps include:
- Building a dimensional model around a business process.
- Using data dimension tables.
- Ensuring that all data rows have the same level of granularity.
- Implementing conformed dimensions when possible to reduce redundancies.
- Building summary views ahead of time to eliminate the need for manual report building.
- Handling schema complexities before data makes its way into a business intelligence tool.
Following best practices will result in data that’s cleansed and ready to deliver helpful findings. But it’s also worth mentioning that some tools, like ThoughtSpot, already streamline some of this process, using a relational search engine to incorporate all of an organization’s data and make custom calculations on the fly. These types of tools bypass the need for pre-set summary views, which can be time-consuming to create and suffer from a lack of hindsight. They also give each employee the ability to search for answers to their questions autonomously.
Draconian Data Governance
Getting data out of the warehouse and into employees’ workflows is a significant accomplishment, assuming that the data is of quality. Using data that is flawed, incomplete or of otherwise poor quality can change a company’s trajectory. Even worse, if and when a business realizes their mistake, it’ll be that much harder getting the entire organization to trust data in the future. The same thing can be said of old-school, waterfall methods for analytics which made them so slow, that by the time something was rolled out, the business requirements changed. And thus the painstaking process, resulted in little to nothing.
According to Gartner, poor quality data carries significant financial costs, resulting in an average of $15 million per year in losses. The more complexity a business has in terms of units, operations, and regions, the more likely that subpar data is used, and the worse the potential losses.
To avoid this operation-killing mistake, it’s crucial to understand the business priorities from the start and become aligned on which metrics inform those priorities. With goals in mind, organizations need to take a deep dive into the current state of their data and the obstacles preventing it from getting better.
Once roadblocks have been identified, data professionals need to convince business leaders that heightening data quality isn’t a one-time thing, but rather an ongoing necessity. When highlighting areas for improvement, many businesses might wrestle with the costs involved. Conducting a thorough analysis of expenses versus forecasted benefits will clarify which initiatives are top priorities and which can wait.
You might feel like you have your work cut out for you. You wouldn’t be wrong. But your business won’t enjoy the many fruits of analytics if you don’t take that first step to learn what’s preventing actionable insights and positive business outcomes.