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Data Liberation

The rise of true self-service analytics

Self-Service Analytics

Waiting for data engineers to prepare and collect data for data scientists can be a slow process. It requires collaboration with the IT department, slowing things down even further. And the results may not be optimal if the data scientist doesn’t quite understand the business user’s needs, or what insights and correlations will best help them succeed.

In response to this, advancements in cloud technology and the proliferation of cloud migrations has enabled the growth of a new, “self-service” culture when it comes to Business Intelligence (BI), Artificial Intelligence (AI) and Machine Learning (ML) analytics.

True self-service analytics allows business users in an organization to directly access and analyze data with BI tools on their own, rather than relying on data engineers or data analysts.

With a self-service analytics culture, far more people across the organization can act upon data with knowledge, insight, and confidence.

For example, a sales director may want to improve their department’s account expansion numbers based on customer behavior data in multiple dimensions, not simply the previous year’s spend. Or a marketing manager may want to create a campaign targeting companies deemed most likely to switch vendors. Rather than having to rely on trained data engineers to source the data for BI tools and on data scientists to model and predict outcomes, the business users can simply access and start using the data themselves.

Self-Service Culture

Agile businesses embrace self-serve analytics

In an ever-accelerating information age, the companies most likely to succeed are the ones that not only glean the most profitable insights from their data, but do it faster and more nimbly than their competitors. In fact, according to ESG, companies who have successfully transformed their internal data cultures operate with 18x greater confidence: 72% versus 4% report their company almost always makes better and faster data-driven decisions.

Not surprisingly, vendors are increasingly developing self-service analytics products that allow business users to directly access and utilize data that’s aggregated from a range of sources, without requiring them to have a background in technology. When every department is able to apply their own unique expertise to BI and make faster, more reliable data-driven decisions, organizations can reach a whole new level of agility and competitiveness.

But most companies can’t optimize self-service analytics because of the 3 D’s (distributed, dynamic and diverse) of data

Unfortunately, many companies today are unable to implement a self-service analytics culture despite the products available. This is because of the fragmented state of their data. They have many different types of data in many different formats, scattered across multiple and disparate systems and servers. Some data is in the cloud, some is in on-premises servers, and it is often in varying formats and governed by different policies and security practices. Under these circumstances, it is difficult to locate, access, and integrate data for analysis. And if you have incomplete data, or if it’s out of date, the results of your analysis could be unreliable.

Furthermore, most enterprise-level companies have already invested a considerable amount of money in a number of different BI tools. For example, one department might use Tableau while another prefers Microsoft Power BI or Excel. Different BI tools use a range of query languages and display data in slightly different ways. When data with incongruent definitions are combined without being normalized, costly errors in analysis can occur, even when the underlying data is the same.

Next Steps

To become truly self-serve, you need an Adaptive Analytics Fabric

Implementing self-service analytics doesn’t have to mean investing in new systems, cloud migrations, or BI tools. The fastest, cheapest, and most efficient way to enable self-service analytics throughout your organization is to leverage an adaptive analytics fabric.

Adaptive analytics is a new approach to accessing and using all of an enterprise’s data, without having to move it or transform it in any way. Adaptive analytics enables self-service analytics in a number of ways.

  • Source-agnostic
    An adaptive analytics fabric is completely agnostic to the format of the data source. This means your data doesn’t have to be replicated or transformed in any way.
  • Standardized business logic
    Adaptive analytics provides a business logic layer that virtually translates all of your data into a common business language that is presented to your users. This gives your organization a shared data intellect that everyone can tap into. The different branches of your company can not only access and analyze data for their own unique purposes, but also act cohesively, making insight-driven decisions for a shared purpose.
  • Tool-agnostic
    With adaptive analytics, you can use any BI tool you want. You don’t have to bend all users to a single standard for BI software. All of your data will be accessible and queries will return consistent answers, no matter which BI tool you prefer to use, or how many you are using.
  • Autonomous data engineering
    Adaptive analytics also provides you with autonomous data engineering to further enhance your business analytics capabilities. Powered by machine learning, autonomous data engineering can automatically make sure your data joins properly and runs properly without having to be moved. It also observes query needs and builds acceleration structures that serve data for queries in a fraction of the time. Queries that used to take hours or days to run return results in minutes or seconds.

What are you waiting for?

The companies who proactively cultivate a true self-service analytics culture will empower their business users with game-changing analytics at their fingertips, giving them significant competitive advantages. Leveraging an adaptive analytics fabric is the most efficient, worry-free, and cost-effective method to bring the transformative power of self-service analytics to your organization.

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