6 Advantages of

Self-service Analytics

By Greg Silverman, June 15, 2020

For the modern business, self-service analytics tools are becoming an attractive option for harnessing the power of their data in a secure and convenient way. Self-service data analytics tools are in-house, on-premise platforms that enable business users with varying levels of analytics experience to scrutinize data and generate reports on their own. 

By taking control of a process that is typically outsourced or accomplished by an expert team, a self-service analytics platform pulls back the curtain on analysis and reporting and makes it accessible and usable for every business user. Typically incorporated into the business-intelligence stack alongside the data warehouse, businesses seamlessly integrate these self-service analytics tools to eliminate demand-side outsourcing and rely on internal IT for faster, cost-effective data reporting.

It's no wonder that Gartner predicts BI and self-service reporting model users will produce more analysis than even data scientists, but that's not the only benefit of using these tools. 

 

Making analytics accessible for the everyday business user

 

The best self-service BI tools, including those used for predictive analytics, enable the everyday business user to find answers on their own. In fact, BI-Survey estimated that roughly 70% of self-service business intelligence software users are casual users. They don't necessarily have the in-depth knowledge or training on statistical methods, but use self-service predictive analytics tools to navigate data and answer their complex questions.

 

What are self-service analytics used for?

 

While BI self-service tools help users understand what's happening in their business, self-service predictive analytics tell them why it's happening and help develop hypotheses for what will happen next. Beyond encouraging business users to create reports, self-service BI tools are used for a variety of other organizational tasks, including:

●     Data visualization: User-friendly dashboards help identify trends and information relevant to problem-solving.

●     Statistical analysis: Casual and more advanced users may adapt reports and other statistical tools to navigate and analyze data, run models, and come to their conclusions.

●     Operational tools: These tools help business users make operational decisions and gain insight into reports.

●     Customer service: Other software offerings and CRM packages help users view data on their customers and spot trends and opportunities for improvement.

Ultimately, a self-service analytics framework allows business users without an extensive knowledge of IT or data to work with large amounts of information.

 

The advantages of using self-service predictive analytics

 

The advantages of self-service predictive analytics go beyond just its ease of use to find results. 

1. Protect mission-critical information: When companies turn to consultants or other outside parties to handle their predictive analytics, they hand over an enormous amount of mission-critical information. Not only are they entrusting these parties outside their secure network to provide results, but they're also relying on them to protect their unique data. 

Typically, in these relationships, the consultant will provide the information the business users seek - but there is no internal learning occurring at the company. On the other hand, the consultant or other party is mining data and creating models that are also benefiting their business. They are essentially able to do their learning with another company's data and use it elsewhere. Also, considering the threat of data being breached and IP being stolen, there's no better way to protect a business's critical information than keeping it securely within the company's own firewall.

2. Enhance team's learning ability: When demand-side analytics are run by consultants, internal business users lose the ability to learn about the analysis that is ultimately informing their decisions. A passive transfer of information from business to consultant and back to users, while beneficial to all parties, does not allow the company to build competitive advantage through learning. Self-service predictive analytics tools help users in making informed hypotheses and updating their models and predictions based on feedback and results. This virtuous cycle creates a sustainable internal model for knowledge growth and company-wide understanding to expect the unexpected.

3. Improve speed to answer: In this day and age, receiving quarterly or even monthly reports has low value. The reality of the market is that every day something new and unexpected arises and changes the underlying conditions of any developed model. Advanced analytics with self-service delivery and insight from machine learning handles fluid information and produces results in minutes when decision-makers need it the most.

4. Enable internal collaboration: Self-service tools shouldn't solely provide results; they should foster collaboration and create discussion among departments that never organically existed before. The upstream work from data scientists mining information is used by all departments on reports that will make their way into the hands of decision-makers, and all parties collaborate to agree on data sets and goals to yield faster, better results. Without relying on an outside consultant to generate reports, internal teams turn to self-service tools science and learn to communicate their desires. Teams work together to improve the predictive model, which builds consensus and greater respect for the work of other departments.

5. Lower costs: Consulting is an incredibly lucrative business, so imagine what an enterprise could save by bringing this service in-house. For instance, it is estimated that 30% of each media dollar goes to the administration to spend it. In-house planning tools and attribution systems hold the promise of reducing that cost by up to 50%. Other areas of cost reduction include consulting projects, agency fees, and offsite hosting costs.

6. Protect internal data: Unfortunately, the more hands business data passes through, the more likely it is to be breached. More than half of all data breaches are likely linked to third-party attacks. Keeping data in-house ensures it's protected by the internal IT and security team alone, who are completely dedicated to patching weaknesses and safeguarding the information belonging to the company. 

When an analytics team is presented with a self-service predictive analytics solution that promotes collaboration and is easy to use, the entire business benefits. Start transforming the way your business makes decisions in-house today with Concentric: Contact us today to learn more.

RELATED CONTENT