Many businesses are stuck in the crosshairs with their data analytics efforts.
Fully (and correctly) convinced that growth and innovation lie through data, they begin implementation quickly. Collection, processing, and storage methods are established, but the business is overwhelmed. The data is there, but the knowledge-discovery process is too burdensome to deliver accessible insights.
This is an oversimplified but accurate account of why between 60-85 percent of enterprise analytics initiatives fail. It’s also 100 percent of the reason why workplaces need easier access to data.
If your company’s struggling to get value from your analytics program, democratizing data may be the missing link.
Here are three steps to democratize your company’s data.
Break Down Information Silos & Bottlenecks
In traditional analytics programs, knowledge runs through the veins of the data team. They are the cogs that power information sharing in a company. Anytime an employee has a question, they submit a report request, and the data team adds it to their queue.
Depending on the number of report requests and the complexity of the query, it could take days to weeks for a report to come back. By that point, the employee may have pivoted to another initiative. If they do use the data, it’s unlikely the report will be leveraged across the department or business. It’s these types of inefficiencies that demand more fluid information sharing. For firms to change their ways, they’ll need to dissect how their workplaces function and highlight all the flaws within.
Give Employees Self-Serve Access to the Data They Need
Understanding how the workplace functions (or doesn’t function) is a critical step to improving communication and sharing in the company. To provide more employees with the means of democracy, however, a business will need a tool that connects non-technical end users to data.
Search-driven tools give users the ability to ask questions and receive instant answers. Data analytics software like ThoughtSpot even comes with voice search features so employees can interact with their company’s information like they do with their virtual home assistant.
These types of tools also deliver answers via custom visualizations to accelerate the takeaways. When employees don’t need to go through extra channels to get answers, morale improves, and they become more effective in their roles. The result is an organization that moves at increased calculated force.
Leverage Machine Learning to Improve Insights
With communication fluid and every employee using data to answer their next question, the knowledge-discovery process appears to be complete. Not quite. The abundance of data the typical company has these days makes it impossible for humans to extract the full extent of knowledge that exists in their raw information. Even trained data professionals take days to build complicated reports. If users are to access and understand insights beyond their initial questions, machine learning needs to be involved.
Machine learning (ML) algorithms feed off large data sets and deliver whatever they’re designed to do. When a user asks a question, ML algorithms automatically run thousands of queries on the entire data set, analyze the data and serve the relevant information. These algorithms can also be used to tailor a search experience to a user or alerting them to findings that may have been missed, such as an anomaly or causal relationship between data sets.
Data generation will keep accelerating. Companies that haven’t figured out how to derive value from their analytics will be left behind. Organizations that are looking to compete for the long-haul need to break down communication barriers in their workplace and share information fluidly.
