Inside Advanced Scale Challenges|Monday, June 25, 2018
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How to Boost Confidence in Data Analytics 

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Ideally, data would be the foundation of every business decision: for optimizing operational processes, for analytical insights and for competitive advantage. But a recent TDWI survey exposes a very different reality. According to the report, only 11 percent of respondents said that they were very satisfied with their companies' investments in data and analytics projects to meet strategic goals for enabling data-driven decision-making or actionable customer intelligence.

This is a harrowing realization for executives, who, according to IDC, are expected to spend $187 billion on analytics by 2019, and for data users, who spend 80 percent of their time just prepping data for analysis, according to another recent report from CrowdFlower. Such poor confidence in data analysis leaves decision-makers not only frustrated with their investments, but leery of their information and insights – forcing many to resort to gut instinct, rather than fact.

But all is not lost. According to the TDWI study, there are three changes business users can make to create a defined collaborative data strategy and avoid the pitfalls of poor data quality and lack of trust in analytical outcomes.

  1. Self-service

The IT department is great at many things – governance, security and organization. However, IT’s need for control can be a bottleneck for analysts who require access to data quickly and efficiently. Increasingly, analysts are turning to self-service business intelligence (BI), visualization and data preparation tools, but when it comes to raw data needed for deeper analysis, the report revealed that self-service tools often fall short.

In fact, less than half of TDWI respondents (44 percent) can find and access relevant data in a self-service fashion. Even fewer (28 percent) can access and analyze new data, including external data, without close IT support. Perhaps most troubling is that only one in five said personnel in their organizations can identify trusted data sources on their own, and only 18 percent can determine data lineage – i.e., who created the data set and where it came from — without close IT support.

For self-service initiatives to thrive, IT and chief data officers must enable analysts by listening to their staff’s needs, overseeing their experiences and pointing them to trusted, well-governed sources for their analysis.

  1. Formalize Processes

For most businesses, it’s unclear what happens to the data from the time it originates to the final analysis. The survey found that analysts most often rely on “tribal knowledge” to find what they need, communicating and sharing data either by email (48 percent) or word of mouth (45 percent).

While tribal knowledge is valuable, it fails to encourage a formalized, governed process for capturing information. Data may be entered haphazardly, or not at all. Plus, many companies are vulnerable to employees who leave and take tribal knowledge with them, leaving organizations susceptible to inconsistencies, governance gaps and regulatory problems.

Instead, employers must develop formalized systems for sharing knowledge. This can take the form of a master data management system, which about a fifth of TDWI research participants report using, or a data marketplace, which only 5 percent of respondents use. However, thanks to the rise in the use of cloud solutions, data marketplaces are gaining traction, making it easier for users to find, comment and review data sources.

  1. Enable Teamwork

Teamwork makes the dream work. According to the survey, most research participants (85 percent) share reports, dashboards and other visualizations, and over two-thirds (68 percent) share data sets. However, the study found that only 22 percent engage in rating and commenting on data sets, reports, dashboards or other visualizations, and just 16 percent say that users and analysts rate or comment on output or results.

Connecting and communicating as a team can vastly improve how colleagues and departments work together. Better communications means errors and duplication of effort can be avoided, resulting in better improved efficiency, data integrity and time to insight.

Organizations should provide socialization features similar to those found on social media platforms. Making team support and team communications an important part of governance and stewardship is the key to realizing the full potential of data.

Conclusion

Data is the lifeblood of critical organizational activities, and given that many businesses expect to spend even more on data analytics in the coming year, the need for better data processes cannot be ignored. The solution lies in having more trust, more data and more minds.

By promoting intelligent data strategies that include empowering analysts through self-service platforms, sharing information in a reliable and accessible location and encouraging teamwork across teams, organizations can improve their data quality and, more importantly, have confidence that their analytical outcomes are trustworthy. Trust unlocks data’s full potential, and intelligent data strategies are the way to get there.

Jon Pilkington is chief product officer at
Datawatch.

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