This is the world where data reigns supreme. Major businesses are leading their respective markets not only because of their great products and services but also because of their ability to make data talk. That’s why “analytics” has become a bit of a fascination for small and large businesses alike. Analytics is now accessible and affordable for all kinds of organizations. This means that in order to truly drive innovation based on advanced data analysis, companies need to move a few steps ahead, and not merely depend on their canned analytics solutions. Enter self-service analytics.
Where does self-service analytics fit in?
Oftentimes, the real impact of an analytics solution on your business’ bottom line is not based on the technical complexity or sophistication of the application. Instead, it’s dependent on the extent of adoption of the analytics package, by the end users.
Sadly, many organizations observe massive gaps between their expected adoption and actual adoption. The reasons could be many, including applications that are not user-friendly, users who are not aware of the existence of the application, inadequate or zero training, long wait times to get new reports and dashboards, or the inability of IT to resolve bugs and add new features.
Among these, the lack of user-friendliness, inaccessibility, and rigidity (not easy to customize) of analytics solutions are the major causes of dissatisfaction, and hence abandonment of these solutions, by end users. Where your canned analytics solutions fail or aren’t up to snuff, self-service analytics can step in and deliver distinct business benefits:
- Making the power of data, reports, and dashboards available to all users.
- Helping users create custom reports on their own.
- Removing unnecessary information from dashboards to make them more useful.
- Reducing cost of IT that’s otherwise spent in supporting user requests for minor changes to reports.
- Enabling access to dashboards from remote locations, via mobile apps.
The business case for self-service analytics is loud and clear. To make self-service analytics successful in your company, focus on these key components:
Without data governance, there’s no way you can rely on analytics. Governance brings quality control, uniformity, and accuracy to data. The only way a company can let end users define their own expectations of data and give them means to realize those expectations, is when it trusts its data’s integrity.
Metadata is a key component of data governance. Metadata helps developers define datastreams for easier understanding by business. Layers of metadata contribute a lot toward ensuring that information within the analytics framework remains coherent.
The metadata approach helps organizations make it easy for end users to feel confident in using data streams. When developers, end users, and managers are confident to rely on the metadata, they’ll all be able to create custom reports that remain coherent with each other.
The single biggest reason for self-service data analytics is that it prevents delays in service of user requests for valuable reports and dashboards. When you bring in a self-service solution, make sure your processes don’t defeat the purpose of its implementation.
Ideally, users should be able to find solutions to their problems using your self-service analytics solution in real time.
The self-service software should deliver information and answers faster than any other source. All you need is a mechanism to track any customizations and new dashboards generated by end users, and business use cases along these same lines, so that any similar/same requests can be served using those custom dashboards.
Access to all relevant data sources
Your self-service analytics will fall on its face if it’s not fully loaded in terms of its data access capabilities. Self-service analytics needs to bring together all the relevant data sources, including contextual data apart from traditional data models and relational sources.
When each data source is also backed by metadata, it becomes all the more value-adding for end users.
IT and end-user partnerships
We mentioned how self-service analytics should not be dependent on IT for its success, and instead should be the first point of refuge for end users looking for reports and dashboards. Well, in reality, IT has a clear role to play, even in making self-service analytics solutions successful.
- For starters, IT will need to hand-hold key end users in the first few weeks of the implementation of the self-service analytics. This eliminates the learning curve for key users, helps them understand the nitty-gritty of the new analytics tools, and motivates them to spread the word among their teams.
- Then, IT can help end users by building a library of commonly used formulas and mini-reports. These analytics assets must be well documented and made accessible, along with usage instructions, for end users.
- Thirdly, IT needs to develop a mechanism to evaluate and safeguard reusable analytics assets created by end users (such as inventory management dashboards made for plant A, which can be rolled out to plants B and C, by merely changing the currency).
- Plus, IT needs to prepare training material (including user guides and videos) to help end users get accustomed to the self-service tool, and refer back to these documents to deal with brewing troubles.
Mere dashboards and reports don’t deliver value; people need to brainstorm, collaborate, question, contest, and rethink to be able to draw insights from data. Your self-service analytics solution will be deemed all the more successful if it offers the avenues of collaboration to end users.
From in-report commenting to dashboard specific group chats, from text analysis boxes to cross-format conversions of reports — modern self-service analytics solutions bring together useful features that enable collaboration and make the application a roaring success.
Massive enabler of success
Data-driven decision-making is a massive enabler of success for organizations. When your company eliminates all kinds of delays in the enablement of data-driven decision-making, it speeds past competitors. Self-service analytics delivers this advantage of speed.
To make self-service analytics successful in your company, make sure you understand the key components discussed in this guide.
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