Getting the Most Data Analytics Bang for Your Buck

It’s important to get the most value out of your expenses when running a business. So, you’re constantly analyzing the return on each investment your company makes. Data analytics is no exception to this rule. These are a few ideas for getting the most data analytics bang for your buck.

Ability to Integrate Various Types of Data

Data comes in many forms and from many places. Many enterprises have long-term backlogs of built-up data that hasn’t been put to good use for this reason. Finding ways to integrate all that data, however, can provide massive benefits to any organization.

There are likely insights going totally undetected when data remains overly siloed. Getting the right analytics software can help bring all those disparate points together to create value.

Ad Hoc Analysis

With new technologies coming on the market, there are becoming more ways for people to work with data directly. One of the best examples of this trend is ad hoc analysis. Modern BI platforms such as ThoughtSpot are empowering employees to ask questions and get answers whenever they pop up using search analytics — rather than having to wait on data specialists to create a static report for them.

It’s far more efficient and user-friendly this way. It often took a long time for requests to get to an analyst, who was often working with a substantial backlog. Then they had to be processed, put into a report, and sent back. If they didn’t exactly answer the query correctly, the process would need to be started again. Have a follow-up question? The process would start over again.

Ad hoc analytics also helps to foster a data-centric business culture, since all employees will be able to actively participate. Some useful — and maybe even surprising — insights can come from people when they’re given the right tools to dig into data.

Embedded Analytics

The actual analysis aspect of working with data is just the starting point; the next steps are sharing those findings with other stakeholders and making decisions based on that mutually accessible information.

Embedded analytics is the answer here because it allows for data visualizations to be taken from an analytics interface and placed into a business app or workflow tool that’s already used for making decisions. This is hugely helpful for getting those critical insights in front of people who need them.  

Pushing Insights into Workflows

Artificial intelligence (AI) tools in data analytics are capable of automatically uncovering insights, then getting them in front of employees. AI algorithms can dive into data, discovering insights as they go. Then these tools can push notifications into existing workflows. The result? People making decisions are able to see automatic insights as they arise, then act on them as needed.

Scalability

When it comes to getting the most bang for your buck, few things are more important than scalability. You don’t want to be spending money on features when you’re not truly using them. At the same time, once you need greater capabilities, it’s essential your analytics platform can seamlessly grow along with your data requirements.

Ensure an analytics tool offers flexible and comprehensive scalability before you invest in it. Otherwise, you could end up having to scramble to get a different one at some point down the line.

Variety of Visualization Tools

Data visualization models are important for helping lay people understand what’s being shown to them, in the form of an interactive chart. Without proper visualizations, decision-makers aren’t going to be able to adequately understand the analysis to its fullest.

Ultimately, data analytics help influence how an organization runs. People need to get data in a variety of digestible formats in order to make the right choices — and they need the ability to keep clicking to drill down, getting a comprehensive view of what the data’s saying before jumping into action.

Data Governance and Security

The safety of data needs to be accounted for when using an analytics program. Data governance is one important piece to this puzzle. You don’t want crucial data to be exposed to the wrong people. Having customizable built-in permission levels for users ensure people will only have access to what’s actually useful to them.

Additionally, security is a paramount concern. Data can be used against you in a lot of ways if it’s obtained by a negative source. Make sure you only choose platforms that take cybersecurity seriously.

There are a lot of things to think about when it comes to using data analytics in your organization. Keep these ideas in mind if you want to get the most out of these tools.