Three Strategies for Predicting the Future with Data Analytics

Optimizing your Analytics Approach for Greater Data Value

Mike OrtmannOctober 30, 2019

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The ability to accurately forecast future financial earnings, anticipate when a customer is ready to upgrade or at risk of leaving separates business leaders from the laggards. Companies that regularly use data analytics to track consumer spending habits and revenue patterns are better equipped to predict their business future, fuel innovation and gain a competitive edge.

Successful businesses today use past behavior patterns to foresee future customer actions, operational needs and potential inefficiencies. Business users across industries spend hours sifting through their data to uncover customer insights, market trends and opportunities to innovate and optimize their business. Still, there is an array of obstacles preventing even the savviest organizations from executing timely analysis to turn raw data into actionable insights and improve performance.

Despite rapid data analytics advancements in recent years, organizations often face three barriers that prevent them from turning their data assets into meaningful insights:

  1. Inefficient data processes
  2. Lack of business alignment
  3. Poor data quality

Inefficient Data Processes

Many companies struggle to effectively maintain and manage their enterprise data supply chain. The speed of today’s business demands that data be created, ingested, shared and distributed at a rapid pace. The complexity of the processes required to deliver data from creation to consumption presents numerous challenges as organizations work to quickly translate information into actionable and trustworthy insights.

With so much data available, the burden of managing, preparing and analyzing large-scale data volumes regularly falls on IT because they are often the only organizational resource armed with the requisite knowledge and technical expertise. However, it is business users, not IT, who use data to make better business decisions and improve the customer experience. They need to understand the data, interpret findings and integrate it into their reports. When IT staff describes data assets using technical languages like SQL, Java, Python, etc., business users are left confused.

Instead, organizations need to formally align business and IT departments to enable business users to get involved in strategic data analysis. The latest generation of data preparation and analytics tools enable business user involvement as never before, delivering self-service data analytics solutions that bring unprecedented visibility and simplicity to the data analysis process.

Lack of Business Alignment

 Empowering business users to leverage their data requires companies to foster a culture of collaboration across diverging lines of business and IT resources. To accomplish this goal, organizations need the support of an enterprise data governance framework to increase their understanding and ability to leverage enterprise data. Data understanding, collaboration and the right tools promote additional data utilization for enhanced analytical insights and improved business outcomes.

Data governance establishes data accountability by assigning responsibility and ownership for all data assets. Policies for data access ensure that data is available and accessed appropriately among all users. Governance initiatives should encourage an enterprise-wide approach to data understanding by merging IT, disparate lines of business and data assets. When different departments work together to define and catalog data, it builds agreement on data definitions and eliminates any confusion business users face analyzing their data.

With full participation across the enterprise, data governance delivers transparency into an organization’s data landscape. However, companies still need to ensure they have high integrity data for accurate analytical insights.

Poor Data Quality

Data integrity is essential to achieving data analytics success. As data traverses the complex data supply chain, it is subject to new uses, processes and transformations, all of which may alter data integrity. It is crucial to implement data quality rules within and between systems within processes throughout the data supply chain rolling up to a comprehensive data governance framework.

By embedding data integrity checks for reasonability, timeliness, balancing and reconciliation and statistical controls directly into data governance, organizations can quickly score and monitor data quality to stop data issues from occurring and proliferating downstream. With high quality, easily understandable data, business users are empowered to quickly define, track and manage all aspects of their data assets and predict future business outcomes.

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