A business’s data strategy is closely linked to their size. A small business that’s just starting to incorporate data into their decisions will have a very different data strategy than an enterprise-scale business with hundreds of big data sets.
When a business first implements their business intelligence tool, and their first data strategy along with it, they often don’t think too much about scalability and growth in the future.
For instance, a small business isn’t going to spend much time considering their big data options, and mid-size businesses aren’t going to worry too much about data warehousing.
As businesses implement their first tool, they’re going to implement the data strategies that make the most sense to them. That means they’re going to formulate and adapt their strategies based on the resources they have at that point, not resources that they hope to obtain at some point in the future.
This is a good strategy for hitting the ground running after buying a BI tool, but it can lead to some problems in the future, as a company grows. As a company gets larger, their priorities shift, they hire more employees, executives build out more departments, and so on.
When these changes happen, businesses need to incorporate them into their data strategy. Otherwise, they might find that whole departments are being left out of the data conversation, or that their collected data doesn’t line up very well against their current goals.
However, many companies start to run into problems when they start to discuss adding more employees to their BI tool. Most BI tools sell their tool per-user, meaning that businesses have to pay additional fees for every set of user credentials that they issue.
When businesses first implement their BI tool, they usually only buy the amount of user seats that they think they absolutely need. As the business starts to grow, however, problems with access start to crop up.
Up until recently, businesses at this point had two main choices – they could buy additional user seats, and gradually increase the overall cost of their tool as they grow, or they could limit access to their tool to only a trusted few, and let data insights flow down from above.
Neither option is particularly appealing to businesses who want to use data to drive decisions at all levels while still keeping costs low. This is why BI vendors have begun to introduce strategies like embedded analytics, which allow users to access data without incurring additional costs.
These sorts of strategies are part of a larger trend called data democracy – allowing greater access to data at all levels of an organization, so that every employee can use data to drive their decisions.
What is data democracy?
Data democracy is a trend in business intelligence and the broader data industry that moves away from the top-down, centralized data strategies of the past towards freer, bottom-up, data-driven data strategies.
All these features make it possible for businesses to give data access to more people within their organization, while also keeping costs down. From there, they can figure out the right balance of access and cost for their situation.
Implementing data democracy strategies can massively benefit businesses that might otherwise struggle to make meaningful use of the data that they collect and store. With a more bottom-up approach to data analysis, businesses can get insight from every level, not just from senior managers or executives.
Data democracy has three main keys — increasing access to data, allowing users to perform their own analyses on data, and encouraging data curiosity at every level of an organization.
Increased access to data
Data access is a broad spectrum, and it’s up to each business to decide where their business should lie along it. Some businesses take data access very seriously. Average employees might not even know their KPIs, just how they’re trending and whether or not that’s good or bad.
Other businesses take a freer approach, allowing employees to interact with any data set or data stream that doesn’t contain sensitive information, even if it might not specifically apply to their position.
The best option is going to depend on a business’s situation and what level of insight they hope to get out of their average employee. There are a few strategies that businesses can use to boost data access to their employees, while still keeping their costs down.
One of the most popular ways that businesses increase the visibility of useful data is through embedded analytics. Embedded analytics is a style of BI implementation that allows businesses to build dashboards and visualizations into external pages, bypassing the BI tool altogether.
This allows businesses to give huge groups of their employees access to data from their BI tool, without any added cost other than the additional cost of an embedded implementation. At the very least, it allows users to keep track of their KPIs and other important metrics in real time, instead of relying on reports.
Some embedded implementations go a step further. They allow users to edit, filter, and configure the dashboards and visualizations that they see on their embedded page. These embedded ad hoc implementations are part of a broader trend to allow the average user to do more with their data.
Ad hoc, self-service analytics
Giving users access to data is useful, but in practice, it’s best if they also have the ability to analyze that data in ways that can bring them fresh, personal insight. However, until recently, data analysis could only be performed by data scientists and those with extensive training on a tool.
Fortunately, all that is changing. Most of the market-leading BI tools are moving away from the extremely complicated, data-science-heavy model, towards a model that prioritizes intuitiveness and usability by a layman.
This new model is called ‘self-service BI‘. With a self-service tool, average users can build their own dashboards and populate them with visualizations powered by analyses that they performed themselves. This way, every employee can be a data scientist, not just the people who have extensive, expensive training.
That isn’t the only situation where modern BI tools have improved the data analysis experience for end users. Another place where many users have difficulty making good use of their data is in situations where they’re trying to customize or reconfigure baked-in reports or visualizations.
In older BI tools, and in many modern non-BI business tools, users can’t customize or reconfigure the pre-built reports and visualizations that came with the tool. This massively limits the usefulness of these reports, since users can’t adapt them to better fit changing situations.
In modern, self-service tools, however, users do have the ability to edit, reconfigure, copy, and build out new cards based on visualizations and reports that users have made. This ability to build out new analytics based on existing ones on the fly is called ‘ad hoc analytics.’
Ad hoc analytics is a powerful tool for building a more flexible data culture. With this ability, users can build out their own analytics based on any other analytics that already exist on the business’s BI instance or build completely new ones based on simple rules.
Of course, this increased flexibility in data analytics is only useful if employees are actually comfortable interacting with the tool, know which questions are worth asking, and understand what a valuable answer should look like. These problems can’t be solved with software — this is where company culture comes in.
Promoting data literacy and curiosity
New BI features, implementation styles, and use cases can go a long way to helping businesses democratize their data, but the businesses have to take it over the finish line themselves. They have to build a company culture that prioritizes data literacy and data curiosity.
Data literacy is the ability to understand the implications of a given data analysis. Employees who are data-literate can read dashboards and draw insight from them, they can build their own dashboards to answer new business questions, and they can answer in-depth questions about the meaning of the data that they’re using.
Many people can read charts and graphs, but it’s much rarer for people to be data-literate without explicit help. Most employees at an average company might be expected to read a dashboard to understand their KPIs, but they might struggle with more than that.
To move towards a really data-driven culture, businesses need to encourage data literacy among their employees. Often, this means having explicit training sessions where participants are taught how to use their tool and how best to answer business questions.
Data literacy can also come from increased familiarity with the tool. Even just interacting with a business intelligence tool, even in simple ways, can help to boost data literacy. If you’re trying to drive more data democracy, it could be a good idea to allow users to experiment with your tool in a free, self-led way.
Data curiosity is the next step beyond data literacy. Data literacy is about teaching users to understand data at a deeper level, but data curiosity is about making employees actually want to analyze and interpret data in a self-led way.
It’s about moving from giving explicit instructions on what analyses to perform on what data to employees doing their own self-led data work to find fresh insight and novel analyses.
Data curiosity is a very hard thing to build in employees at scale, and some employees will naturally be more curious than others. This is a situation where company culture plays a major role.
Are employees encouraged to work autonomously, or are managers watching their every move? Do people feel like they can speak up and give input, or do orders come down from on high? Do employees feel empowered and supported in their roles, or do they feel replaceable?
The best strategies for encouraging more data curiosity are those that encourage employees to feel like they can make decisions with their own expertise, that they can give input or suggest new courses of feedback, and that they have the freedom to explore their data.
Data democracy – empowering every employee to drive insight
The result is a business that’s using data to drive every aspect of its operations, at every level. Beyond just billboarding KPIs or running reports, it means that data can transform every operation and improve every workflow.
Check out some related resources:
Modern BI for All Field Guide: Data Agility
Building Data Integrations on a Modern BI Platform
The Third Wave of Data Architecture Design: Decentralized, Frictionless, Self-Service Access
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