Where Businesses Go Wrong with Data Prep and Analytics

Learn the Three Main Obstacles Standing Between Data and Better Business Insights

Robert CherneskyJuly 24, 2019

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Taking advantage of data and analytics to track market trends, develop customer insights, monitor the competition and gauge performance is crucial for business success. Effective data prep and analytics are key to insights-driven business.

Businesses leverage data across the enterprise, in order to turn their information into reliable insights to make educated and strategic business decisions for operations management, customer engagement, growth strategies and more.

Still, even some of the most prominent organizations across the globe fail to realize the full potential of their data. They often run into three obstacles with preparing data for insights:

  1. Time Crunch: It’s taking too much time to get the data prepared for analytics
  2. Scope Creep: You ask for data and then ask again as the project changes
  3. Cost: Complex IT-oriented tools are expensive and require specialized resources

Something must change to accelerate data prep and analytics results. Here’s a hint: It’s decentralization to reduce time and cost.

Managing and Maintaining the Data Supply Chain

 Controlling the modern-day data supply chain is a challenge with vendors, partners, service providers and other third parties that are continually exchanging data – especially sensitive data. When data is regularly ingested, created and transformed, and constantly in motion across an enterprise, it is often a challenge for business users to quickly convert data into actionable and trustworthy intelligence.

Poor data quality, data inconsistency and a lack of data transparency are all legitimate concerns businesses face. Verifying data for accuracy and consistency throughout data’s lifecycle is essential to ensuring that all systems are in sync, data formats are correct, and users can quickly and easily access data analysis and confidently build data-driven strategies.

While navigating the data supply chain is complex, businesses must also overcome other issues such as contending with legacy technologies. Often, they are slow and error-prone. Outdated systems are not only costly, but also make it difficult to put the power of advanced analytics directly in the hands of business users.

Eliminating the Reliance on IT and Overly Technical Tools

In a modern data-driven organization, business users are the leading consumers of enterprise data. However, the IT department is often the only line of business equipped with the tools and expertise to manage, prepare and analyze data on large-scale projects. Business users receive the reports and are then left confused by technical IT languages such as SQL, Java, Python and more.

Dependence on IT is reinforced by businesses who leverage traditional extract, transform and load (ETL) technologies for data prep and analytics. While these tools help facilitate data movement in critical processes, they do not establish collaboration between IT and disparate lines of business. Furthermore, they are typically highly technical tools that can’t be used or understood by your average business user. Because of this, they’re often a barrier between business users and strategic data analysis.

These challenges are universal, and examples span all industries and organizations.

What Does IT Reliance Look Like?

 In any business, each department has different objectives and goals for data and analytics initiatives. Marketing may need data for targeted email campaigns, but sales teams use data to make the right offer to the right client at the right time in the sales cycle. Users from both marketing and sales lack the technical expertise to use their organization’s ETL tool, so they both must depend on IT to get the data they need to achieve their goals.

IT is already working on numerous inquiries from legal to ensure regulatory compliance requirements, and questions from human resources so they can narrow down possible job candidates. By the time everyone receives their requested data, weeks pass, and the data is now outdated and no longer useful.

Instead, businesses need new tools and technologies to deliver data to business users within hours, not weeks, and alleviate IT’s massive backlog of requests.

Transitioning Toward Agile Tools and Techniques

 Since business users don’t often possess the expertise needed to use ETL tools for data analysis, businesses must transition toward modern self-service data and analytics technologies. By selecting an all-inclusive data intelligence platform, organizations eliminate time-consuming, manual data prep procedures to deliver data to business users in real time – without IT dependence.

With a fully integrated data intelligence platform, enterprises deliver business users a self-service data experience that incorporates the crucial data management capabilities of data governance, data analytics, and data quality to meet their data needs, without long wait times. Business users are empowered to quickly access data sources to acquire and parse that data in minutes. In addition, business users can visually collaborate as they extract, prepare and analyze data from multiple sources to create a rich aggregated data set for analysis.

Transparency into every process step and automation of data blending and cleansing enable users to profile, aggregate and transform data. It allows business users fulfill their own data requests to meet crucial deadlines, instead of waiting 20 to 30 business days. The automation of manual data processes also frees up IT resources for other projects, creating additional lines of revenue, and ultimately, a competitive advantage.

Are you looking for additional information about solving data prep and analytics challenges? Check out the case study below, or experiment today with the free version of our tool: infogixcom.kinsta.cloud/desktop

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