Six Strategies for Data-driven Teams to Master BI and Advanced Analytics

By mastering business intelligence and advanced analytics, organizations can stay one step ahead of the competition. They can employ sophisticated modeling techniques for improved operational efficiency, investment decisions, and customer experiences. Here are six advanced strategies for actionable insights from data that organizations can adopt.

While numerous organizations have made considerable investments in analytics technology, few have realized a significant return on their investment. Most C-Level executives would agree that data is essential to business decision-making, even though very few of those executives will admit that they trust their company’s data when it comes to making decisions. 

Relying on one’s experience, intuition or the status quo to make decisions no longer prevails in today’s digital world. To remain competitive, improvements in operations and products should come from deploying insights-driven business models. How can an organization establish trust in its data? 

Here are six strategies to employ to start getting truly actionable insights from data:

What Question(s) Are You Trying to Answer?

Online Dialogue’s Annemarie Klaassen says her “number one strategy to turn data into actionable insights is to ask yourself what you are really looking for. If you don’t have a clearly defined and specific question, you certainly won’t get a clear answer either. If you ask generic questions, you will get generic answers which aren’t very actionable. Ask specific questions and you will get specific actionable data!”

When building models for predicting and optimizing business outcomes, you should not start with the data; instead, start with identifying an opportunity to improve performance. Ask what should be the potential business impact of the data. Be specific about the business problems you want the data to address. You’re not looking for just any insights from the data; you are looking for insights that are relevant or important to your business. It will help if you ask the right questions to get the most relevant answers. Thus, let the business side of the house determine which opportunities to pursue, leaving the facilitation of your data strategy to IT. The more people involved in deciding what data is needed, the better it becomes to eliminate any one person’s bias taking hold. 

See More: Can Business Intelligence Become Smarter With Cognitive Support Services?

Let Business Strategy Prioritize the Correct Data

Chris MearesOpens a new window , manager, data integration at ViacomCBS, says that “once the business questions are known, we can then understand which specific data needs to be collected and analyzed. Through this data analysis we can begin to create hypotheses to answer specific business questions and test our hypotheses to gain actionable insights.”

Business priorities should determine what data needs to be available and when. Once those priorities are defined, you may find that some of the information you’ve been collecting and managing over the years is no longer needed. Likewise, the data you do need is missing. 

Begin by asking, what data is available to you now? You may be surprised by what you have. It has been found that most organizations use only a small percentage of their data for analysis. Ask what decisions you would be able to make if you just had the right information? What data do you want to track? Then, identify, integrate and manage all your data sources, both structured and unstructured and in data silos and legacy systems, that will give you the information you need. Don’t forget to include data from external sources, such as demographics and weather forecasts. 

Integration of all these data sources will require common data classification across all business units. Additionally, data segmentation will lead to more actionable insights from data. For example, by segmenting or categorizing client data, you will better understand client behavioral patterns. Besides, be sure to establish the context of numbers in the dataset for determining meaning of numbers, their relevance to answering your question, and their effect on the business. Once identified, any overlapping data needs to be synchronized and merged, and any missing information needs to be resolved. 

Establish the Data Architecture That Will Meet Your Business Goals

Once you have determined your business priorities and the data you currently have to work with, you are ready to collect and integrate all the relevant data and structure the data into information. Your data architecture should be built specifically to your organization’s needs, including the distributed data model to be employed, how the data is sourced and shared across the enterprise, and how it will be analyzed.

See More: How AI-Powered Analytics Can Bridge the Insights Gap

Develop Business-relevant Analytics That Your Managers Will Be Willing and Able To Use

Data analytics, or the process of discovering patterns and trends in data, provides actionable insights for making informed decisions. Your analytics strategy must take into account the distributed nature of data (from cloud to the edge), as well as the key analytics capabilities you want to make available to your employees–everything from traditional reporting to real-time data analytics and machine learning. 

Identify the business outcomes and value you expect from your data analytics and machine learning models. How do your current analytics capabilities match up, if at all? Are your analytics tools being used to support daily operations? Are they easily accessible on time? 

For front-line workers, you should make available tools designed for them, not for modeling experts. Ensure that your analytics and tools will complement existing business processes. In short, the analytics tools should help your front-line workers better perform their everyday jobs. Through training and incentives, they should depend on these tools to identify future opportunities and solve problems. 

Become a Data-driven Organization

Just having data does not make your organization data-driven. Data-driven organizations make their decisions based on facts derived from data analytics, not opinions or emotions. In a data-driven organization, information flows freely within departments and across business units for decision-making and strategic planning. Unless all members of your organization have a common understanding of the data, know which metrics to track from the data, and consistently prioritize data over their own opinions, your company will lose competitiveness. Your organization will ultimately realize maximum business value from your data only when all of your employees are involved in data-informed decisions and products. 

So how do you become a data-driven organization? By making data-driven decision making standard operating procedures throughout the enterprise. First, get executives on board by demonstrating where data challenges exist and how to remedy them. You can then build upon a business case that shows how the data can be used to improve business outcomes. Once trust in the data is established, executives and IT can work together to build a data-driven organization. 

A data-driven organization rewards data collectors. Its executives base their strategic plans on data analysis. Performance is linked directly to clearly defined KPIs, encouraging teams to be aware of organizational data and change behavior to use data analysis to attain organizational goals. Allowing business users access to performance data through shared dashboards that track KPIs in real-time helps build a data-driven organization where everyone views and tracks the same data. But care must be taken when opening up information to business users to avoid security risks. 

Work Towards Continuous Improvement

Data-driven organizations evaluate every decision against the latest data coming into the organization; these organizations continually improve their operations, one step at a time. Following a continuous improvement approach that allows for experimentation and learning from unsuccessful results will encourage your workers to find new ways to apply data and, consequently, respond quickly to business events as they occur. In short, data-driven organizations continue to review their decisions and revise their data models to meet changing business needs. 

Where to go from here

Now that you have six strategies for getting actionable insights from your data, Zorin RadovančevićOpens a new window from Escape Studio advises:

“Start as soon as you can. Iterate in small increments. Build the best multidisciplinary team available which should be truly savvy in the ways of your business as the entire focus should be on predicting business outcomes. Use internal IT or outsource to build a set of tools which represent a collaborative, scalable and simple to use decision support platform. Give your team free and transparent access to data. ‘Force’ them to produce simple insights as soon as possible regardless of the predicted outcome and let the modelling games begin.”

Is your business able to generate enough actionable intelligence from stored data? Let us know n LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We would love to hear from you!