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How Analytics Consumers and Data Experts Can Come Together to Close Insights Gaps

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Read more about author Ajay Khanna.

Data is critical to empowering businesses to make decisions that translate into boosted bottom lines. However, a recent NewVantage survey found that only 24% of the workforce is confident in their ability to read, work with, and analyze data. As a result, many organizations depend on data experts to obtain relevant insights that inform decision-making, which reinforces silos that separate the data experts from consumers of information and creates analytic bottlenecks and costly business decision delays.

The truth is analytics is a team sport. Analytics consumers (such as business teams) must work closely with the data science and analytics teams to generate insights to become more data-driven. With the dramatic increase in the volume and complexity of data in the last few years, organizations cannot rely on the model of tossing analytics questions over the fence only to wait a few hours or a few days for an answer. There needs to be a more modern approach to analytics – one that puts timely analysis into the hands of consumers and fosters greater collaboration between business teams and data experts.

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Here are the top ways that a modern approach can bring these important audiences together to streamline the process for making better data-backed business decisions.

Driving Real-Time Data Conversations with Intelligent Automation

Imagine you are in an executive meeting going over quarterly metrics. Usually, the data shared is in the form of reports or dashboards of standard metrics. Sometimes, if you are lucky, some analysis was performed beforehand to give a deeper understanding of the underlying information in the data. Typically, colleagues will ask questions about small slices of data that have never come up before. Maybe it is about sales in a new territory of interest, how a product line is performing in a certain channel, or why conversions from a given age group are trending down. Because these are new questions, the data team will have to investigate the answers after the meeting, taking hours or even days to respond. The opportunity for quick, informed decision-making has passed.

Analytics tools that intelligently automate data analysis can power this three-way conversation between business, analyst, and data, working quickly to answer questions as they arise in real time. By finding the most important insights and automatically evaluating every combination of data points over billions of records, organizations can be more assured of answers than when they are forced to manually look for insights, test only a handful of hypotheses, and analyze a subset of data. It is easier with intelligent automation to find out why metrics are changing in very granular ways. For example, in sales, this means finding the cross-section of products, regions, or customer groups that are underperforming. Or in marketing, learning the campaigns or channels for specific customer segments that need attention.

Empowering Employees with Self-Service Insights

Organizations also need solutions with natural language interfaces that empower business people with greater access and ability to analyze and visualize data. With natural language search capabilities that enable users to ask business questions like, “Why are Q2 product sales down in New York?” data analytics becomes simplified for everyone. Results and analysis should also be returned in natural language so they are easier to understand, and findings are not misinterpreted – which can happen when results are only visualized.

This level of self-service can also be coupled with automated analysis to allow more people to get deeper insights from data. With this capability, business professionals – regardless of skill level – are enabled and encouraged to perform queries and generate reports on their own, with minimal IT or data scientist support. This also helps resource-strapped companies to minimize the impact of the data talent shortage. With greater self-service, business teams can increase their data literacy to act on the insights faster and more effectively collaborate with data expert peers.

Collaborating with Unified Data Environments

Most organizations have many tools in their data stack – from BI dashboards, spreadsheets, SQL query tools, and machine learning software – that keep individuals of different roles separated and keep them from speaking a common language. In fact, 33% of companies report that identifying a cohesive data strategy is one of their biggest problems, according to a study by Aberdeen. The study also found that 26% of companies have difficulties accessing data from different areas of the business. Not only do these issues keep consumers and analysts separated, but disjointed tools also mean organizations must maintain multiple copies of data and operate a multi-tool workflow that makes iteration and scale difficult.

Modern organizations need a unified approach to the analytics lifecycle, which includes data connectivity, query, machine learning, and visualization. This approach offers analysts the power and flexibility to work with data with their preferred method (such as SQL or Python), while also giving business users the ability to visualize data and monitor metrics. Unified data environments create a smoother end-to-end workflow so people across different roles can more easily collaborate. For example, this would allow companies to build a model that segments customers by their likelihood to renew or upgrade their subscription. These predictions and recommendations can be made accessible to business users through search or interactive visualization to validate findings, provide feedback, and then quickly incorporate the business expertise and fine-tune the model.

To get more people within an organization confident in their ability to read, work with, and analyze data, organizations need tools that simplify gathering and leveraging critical insights across the entire employee base. By democratizing analytical processes, data consumers and data experts naturally work in sync with one another, as data experts have more time to ensure data is prepped and usable for the business, and data consumers can independently gain the insights they need to make fast business decisions. Using intelligent automation to create a collaborative data environment, organizations will see more unified, data-driven decision-making that has an immediate positive impact on their business. 

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