Data can be an exceptionally powerful tool when you know how to harness its unwieldy power and make insight-driven decisions. Deciphering data can be an overwhelming task and many middle market companies struggle to get the most value from it. Being competitive in the long run requires learning how to properly leverage your data by building a robust data framework. How do you make sure you’re getting the most value from your data?

Here are key elements organizations should build into their data framework:
 

 

Define your business goals and challenges. Data is useless without aligning it to a tangible business vision and business strategy.  

• Vision and executive sponsorship – Build executive buy-in on a vision that emphasizes data assets as critical to a company’s success.
• Business case planning – Successfully using data means getting the right data into the right hands with the right business case.  
• Measurement of ROI – Set metrics for assessing the quality and usability of data assets.
• Service Level Agreements – Ensure the team has an appropriate level of support and has a sustainable approach to dealing with data.

 

 

Examine the current data ecosystem; evaluate whether to evolve, re-platform or build a scalable data analytics architecture from scratch.  

• Cloud Readiness – Create a cloud adoption strategy that helps your organization embrace the cloud. Most organizations are leveraging the cloud to drive data analytics innovation. 
• Infrastructure (Cloud or On-Premise) – Understand the data infrastructure requirements needed to support a data analytics initiative and make sure you’re on-premises center is sustainable and secure.
• Data Management Process – IT departments struggle to manage and mine structured and unstructured data across many different data sources.  It is important to establish a data management process to use and manage your data.

 

 

Protecting data has become more critical than ever before as the amount of data created and stored continues to grow at unprecedented rates.  Data protection is the process of safeguarding important information from corruption, compromise or loss.  

• Data Privacy Compliance – Consider privacy compliance obligations when designing a new system, process or AI solution such as instituting Privacy by Design so that privacy is embedded from the onset of a project.
• Data Security – Implement data security requirements needed for new system or AI solutions.
• Data Lifecycle Management – Know the data lifecycle for collecting, managing, sharing, storing, using and retaining information and data.

 

 

 Analytics can be powerful but requires an established process. A scalable foundation for business intelligence and data analytics must be created to manage streams of data to deliver both insight and foresight.  

• Dashboards and Reports – Dashboards and reports that use key KPIs can help make better decisions and show the value data visualization can bring to an organization to help leverage data across the organization.
• User Governance – Data Governance is the interconnection of people, processes and technology, which allows an organization to leverage data as an enterprise asset. It consists of policies, rules to collect, manage and archive data.
• BI Tool Selection & Licensing – Selecting the right software that accounts for a multitude of different cross-departmental needs requires consideration. Having a well-defined software evaluation process and robust best practices will minimize project disruption and the potential of larger projects suffering in the future. Through the power of business intelligence, you can consolidate and connect your data in one place to create rich, shareable visuals.

 

 

Educate your workforce about the latent power of data in improving day-to-day decision making, business outcomes and revenue potential. Help your staff recognize they could make more informed business decisions with access to analytics.  

• Communicating Change – Focus on the end users. Develop a process and plan for helping your organization become data-driven through increasing data literacy and using data driven information across the company.
• Sharing and Distribution Strategy – Create a strategy for sharing data across the organization for end users to consume in the most effective manner, that promotes adoption. 

 

 

AI describes the creation of intelligent machines that work and react like humans. AI doesn’t replace human capital, but it does make your workforce more efficient by taking on part of their automatable workload.  

• Machine Learning (ML) – Within AI sits Machine Learning, through which algorithms can learn from the data they mine, applying their newfound knowledge to improve performance and look at variation across the data.
• Natural Language Processing (NLP) – NLP is a part of AI that studies how machines interact with human language. Combined with ML algorithms, NLP creates systems that learn to perform tasks on their own.
• Operational Planning – Outside of modeling, what is the right process for taking your AI and implementing that into your corporate workflows for maximum ROI and impact?
• Endpoint Integration – What is the strategy for taking data and integrating it into the end points where users can consume the data for real life impact?

 

Establishing a data framework that accounts for these key elements provides your organization with a holistic approach to data analytics. Data analytics combined with artificial intelligence is changing the way businesses understand their current performance, plan for future growth, and most importantly, react to crises. But it all must start with understanding how to manage your data.

BDO Digital’s Data Management and Data Analytics professionals have extensive experience in helping clients optimize the way they manage and derive value from data.  

Take a look at our Data Analytics Accelerator to learn how you can turn your data into actionable insights.