How to Be More Proactive With Your Data

Increasingly data analytics being adapted by companies across multiple sectors because of it’s ability to take inform decisions and plan, track goals of companies against current performance. But, how do these companies will know if their analytic practices are as effective as they could be?

Before companies begin to use data to leverage advantage, they should make sure establishing a proper foundation which will ensure that they will get the most value as possible out of their data. Things like adapting a positive company attitudes toward data and integrating data interactions into workflow processes, among the others, can often be the difference between long-term growth or stagnation.

Let us take a look at how companies can be more proactive with their data.

Adjusting the Attitude Towards Data

Some organizations will only use data scientists and analysts to handle their data initiatives, while the others will give access to multiple organizations and professionals across various departments within their organization. The organizations and professionals which that companies are giving access to their data, need to have a certain type of attitude to make sure their analytics practices are getting the most out of the available data. While implementing data analytics can be a large shock to the system, personnel using this data need to have an intellectual curiosity, a good understanding of how the business can benefit from this data, have good communication skills, and most importantly, trust the data they are working with.

According to Gartner, Advanced Analytics is autonomous or semi-autonomous works on data/content involving sophisticated tools beyond traditional business intelligence (BI). Advanced Analytics is intended to discover deeper insights, make predictions, or generate recommendations. The advanced analytic techniques include methodologies which we often discuss on this website related to data mining, such as deep learning, machine learning, data visualization, semantic and other analysis, neural networks etc.

Many of these advanced analytics can be customized to pull certain information or used to generate specialized reports based on specific queries without the need of professionals having full technical knowledge. This is exactly where the intellectual curiosity comes handy because questions still need to be asked. But, even if data is limited to analysts and scientists, they should always be looking for ways to improve how the data is being used.

Their understanding of how the particular business operates is key the because it will allow them to ask the right questions and find innovative solutions using the available data. Their communications skills have to be top notch because it is likely the coworkers they are passing this information along that do not have access to the data used to generate these reports. Being able to explain how they got from point A to point Z quickly and efficiently will allow those on the receiving end of the data reports the best use of the information given to them.

Creating Good Workflow Habits With Analytics

Workflows are the way peoples get work done and can be described or illustrated as series of steps which need to be completed sequentially in a diagram or checklist. Companies set up automated workflows because it leads to fewer mistakes, quicker processing time, better information tracking and reduced costs.

In order to build good workflow habits, companies should be collecting data from their processes, questioning the data and make changes to the processes as needed. While collecting data, one should watch which processes were completed and also which ones were not completed. If any tasks are not being completed properly through the workflow, they should question why the task is not being completed. Once a solution is available, then these tasks need to be amended so the rest of the workflow process efficiently works.

Good Habits For Data Analytic Tools

When companies get shiny new hardware or software to simplify their workflow processes, it’s easy to fall back into the habit of letting the analytics tools do all of the work. Users need to be creating good habits with these tools by understanding the value of simplicity and the insight power of more consolidated data sources.

It’s easy to let an algorithm do all of the work behind the scenes work when pulling data but if you can’t explain why the findings are important, then it’ll be difficult to form actionable next steps. Make sure to not only inject data into daily decisions but set up a system that allows those decisions to be evaluated later. Data-driven decision making can be powerful but you shouldn’t assume it’s a plug and play operation; various thorns like poor data quality, misaligned goals or lack of vision can all hinder data’s potential.

Conclusion : The Takeaway

In conclusion, advanced data analytics are becoming part of the norm in businesses today but organizations need to think carefully about how they will use data before they jump to opt for such. They should make sure a positive company attitude around data is adapted, that data is weaved into existing workflows and that executives across the company are aligning business goals with analytic efforts will ultimately lead to better analysis, fewer errors and make large amounts of data less cumbersome to handle.

Are you ready to be more proactive with your data?