What this blog covers:
- Why is self-service analytics important today?
- Challenges faced by organizations in achieving self-service analytics
- How Kyvos can help you achieve self-service analytics
Last week I attended one of the BI conferences, where one of the presenters highlighted a challenge when his manager made an ad hoc request to get a list of customers to focus on for their next marketing campaign. He mentioned that when his manager asked him data-related questions, he couldn’t answer them quickly because analyzing billions of data and generating reports was a big challenge for him. So, he had to contact the analytics team to create reports on data at this scale.
While listening to him, I wondered if he needed to call the analytics team to get the report, then how dependent he is on them to access data and get answers to the questions asked by his manager. Now think, for instance, if thousands of data consumers and business leaders need access to data and reports that only the analytics team can provide, how overwhelmed the analytics team would feel? For every request made by business users, they have to locate relevant datasets, run queries, and create visual reports. It takes time. And waiting for reports creates a sense of frustration among business users and other data consumers.
While thinking about it, I realized the answer to all his problems is achieving self-service analytics. His organization needs a solution like Kyvos to make data available to more individuals inside the organizations, partners, third parties, and customers so they can have better insights with no dependency on analytics teams whatsoever.
The answer is to adopt self-service analytics. According to research by Forrester, “between 60 and 73% of all data within an enterprise goes unused for analytics.”
This means while organizations are investing a fortune in data storage, they are still struggling to perform effective analysis to capture data-led insights. One of the reasons why this is happening is their reliance on the IT department or a centralized data team to provide actionable insights.
Therefore, it has become crucial for organizations to implement self-service analytics to drive a strong data-led culture.
Importance of Self-Service Analytics in Today’s World
In the past, when analytics systems were very expensive and constrained by the number of users, organizations were cautious in providing access to data and reports. But today, when organizations want to improve operational efficiencies and need informed insights faster for decision-making, they are willing to provide democratized access to domain experts. This way, domain experts are encouraged to ask questions and perform self-service analytics to uncover specific trends, activity patterns, and actions correlated with each event.
A study in the Harvard Business Review revealed that by making data available and empowering their employees to make data-driven decisions, companies saw more than 10% annual revenue growth.
Today, when organizations have joined the defining decade of data, being data-driven is no longer a nice to have. Due to ever-changing business requirements, uncertainties, natural disasters, and disruption born events such as COVID-19, the world around us has changed too much. It has become critical to shift away from traditional BI tools, invest in self-service today, and put live data in the hands of employees responsible for interacting with customers, partners, vendors, and more.
What’s Holding your Organization Back from Achieving Self-Service Analytics?
Implementing self-service analytics and giving BI ownership to more employees inside the organization comes with its own set of challenges:
Low-Level Data Literacy
An organization that provides data access to novice users faces a serious consequence of poor-quality reports. Therefore, self-service BI requires users to be data literate to understand and interpret the data, make accurate decisions, and drive maximum value for your organization. Low-level data literacy or incomplete understanding of data has large-scale repercussions for your business. It can erode your organization’s competitiveness and damage its market credibility. No organization wants to take such chances.
According to research by Accenture, only 21% of business people feel confident with data.
Being data literate doesn’t mean everyone in the organization should become a data scientist. It simply means every employee with access to data should have complete knowledge of their respective domain data to make intelligent business decisions.
No Data Governance
The enterprise-wide data is open to business users through self-service BI, but that doesn’t mean zero IT involvement. It simply means IT resources are called on less and business users get their reports faster as they don’t have to wait in a queue for reports. However, with data democracy in place, the role of IT in keeping data secure gets eliminated. If your organization doesn’t have robust governance frameworks, it can expose your data to risks of inconsistency. Different users working in silos, analyzing the same metrics with various filters, will deliver conflicting results.
No matter how much an organization wants to provide democratized data access to all employees, it’s essential to embed controls and manage the ethical and trusted use of data while adopting self-service analytics.
Enable Self-Service Analytics with Kyvos
Organizations have seen a surge of self-service analytics tools that enable analysts as well as non-technical users to gain insights and make data-driven decisions. However, investing in analytics tools alone can’t deliver business value. For a Self-service analytics tool to do its job, organizations need to ensure that the people using the tool have access to the data across the organization and are using a single standard for consuming and driving analytics.
Another concern that becomes a barrier while adopting self-service analytics is ever-growing data volumes. Self-service analytics tools take longer to fetch the results due to higher analytical complexities and an increase in cardinalities and the number of dimensions.
Kyvos can overcome all the challenges mentioned above and empowers users to perform self-service analytics on cloud-scale data while ensuring data governance. Let’s see how –
Eliminate Siloed Reporting
Self-service analytics allows everyone in your organization to access data for real-time decision-making. However, with the rising data volumes, it’s becoming more challenging for data scientists and BI users to inflict a single standard for consuming and driving analytics.
To overcome this challenge, Kyvos builds a Smart Semantic Layer™ on your BI architecture, bridging the gap between business users and complex data sources. It enables you to centralize and standardize all your business logic on top of the data layer so that everyone in your team can speak the same language. This way, you can foster a more collaborative environment where every team can have a single version of the truth to rely on, thus eliminating siloed reporting.
Controlled Data Access to Prevent Unauthorized Access
While implementing self-service analytics, it’s crucial to enforce rules to prevent your data from unauthorized data access. Today organizations manage security layers on various entry levels. Wouldn’t it be easy to have just one layer that can provide secure access to your data?
Kyvos universal semantic layer is your answer. It acts as a protective layer for every analytical query fired, irrespective of who is firing it. It provides a native three-tiered security architecture that works seamlessly with cloud platforms and ensures data protection at multiple levels. Since the semantic layer is in between the organization’s data storage system and analytics tool, it enables granular-level semantic data platform access control through row and column level security at the group and user levels.
Final Thoughts
Self-service BI opens up analytics for end users, allows them to pose questions, and helps them to be more productive with the information they want. However, if not implemented effectively, organizations will face the challenges mentioned above and miss out on insights that could help them collaborate, streamline business processes, and improve customer satisfaction.
According to a survey conducted by McKinsey, 43% of high-performing organizations allow data to be broadly accessible to employees whenever needed.
A modern solution like Kyvos can take you one step closer to self-service analytics by providing democratized access to enterprise-wide users. With Kyvos, you can analyze billions of data in seconds and implement governed self-service where business and IT collaborate to establish standards that define data elements and processes for ensuring data quality while maintaining granular-level access control.
A modern solution like Kyvos can take you one step closer to self-service analytics by providing democratized access to enterprise-wide users. With Kyvos, you can analyze billions of data in seconds and implement governed self-service where business and IT collaborate to establish standards that define data elements and processes for ensuring data quality while maintaining granular-level access control.
FAQ
What is self serve analytics?
Self-serve analytics refers to a format of Business Intelligence (BI) where business users can access enterprise data and generate reports without any assistance from IT or BI specialists. This helps line-of-business users make data-driven decisions without waiting for meaningful insights to be shared by the analytics teams.
Why is self-service analytics important?
Providing democratized access to domain experts encourages them to ask questions and perform self-service analytics to uncover specific trends, activity patterns, and actions correlated with each event. Instead of relying on pre-built reports, they can create their specific reports or dashboards to be more efficient and accurate.