The democratization of data is shifting businesses towards self-service analytics. Self-service analytics enables non-tech savvy users or business users within an organization to access data for faster and informed decision making. However, the democratization of data comes with its set of problems that are causing concerns among businesses. According to a report by TechTarget, 61% users confirmed that using a self-service BI (business insight) tool “creates report chaos,” while 42% agreed the tools “confuse users.” A whopping 73% users cited that using self-service tools requires more training than expected. This is contrary to the purpose of using self-service tools for analytics, which means there is a gap in the entire process of adopting self-service analytics.
According to Gartner, most business users would have had access to self-service tools by 2018, and we have come a long way since then. However, even today nine out of 10 initiatives are likely to experience some negative impact on business. Self-service analytics is about the easy use of data by business users for critical thinking and decision making. It is not about generating reports, but getting answers critical to a decision-making process in real time.
It is therefore important for companies to understand what is required to make self-service analytics succeed. It is not about data, technology or sophisticated BI practices. It is about organizational culture. Every organization has a culture of decision making. For success in self-service analytics, an organization’s employees should have the culture of using data to start, propagate or conclude every major or minor conversation. A healthy culture of seeking data in a focused and relevant manner also helps the IT and the analysts to tweak accordingly and refine the organizational self-service BI tool for the users, for optimum results.
To support this cultural change, you need to adopt some self-service analytics best practices such as:
Organizational readiness: Every organization goes through an evolution phase in BI. It is important to assess the level of BI and analytics capabilities an organization possesses to understand the level of analytics its users have. This will help in determining the type of self-service tool required for the organization. Readiness doesn’t just mean the deployment of technology, data readiness; it is dependent on the business to be ready to make decisions based on leveraging data rather than just business instinct.
Internal Collaboration: Self-service analytics actively promotes the adoption of organization-wide collaboration. Primarily between three groups of stakeholders. Collaboration between end-users leveraging the solution, analysts supporting the non-skilled personnel, and the IT professionals managing the overall data ingestion. Intra-organization collaboration ensures the right data is there for analysis, the right insights are extracted from the data, and both agility and accuracy is maintained while delivering crucial business intelligence.
Data readiness: For BI to positively impact a business, believing in data accuracy plays a significant role; therefore, ensure a robust practice of data governance in place. Make data governance part of an organization’s culture and not just aligned or a responsibility of a central team. Collaboration between the IT and business units is crucial with business providing continuous feedback on the data quality levels. Data quality practices also govern a well-defined and easy to understand data definitions. Influencing people in the right way to adopt the data governance culture is strategic to the success of self-service analytics.
Data security readiness: The culture of using and accessing data from any device, location or time will grow in future. To enable anytime, anywhere access to data, data security, compliance, and data access rights should be well taken care of at the time of making a transition to self-service analytics.
Technology readiness: Technology changes fast. It is important to find out the kind of BI platform and tools an organization is using. The level of technical competence the users have in terms of BI, user’s adaptability and willingness to use new technology. Technology should also account for scalability of BI solutions right from the start. With changes within the organization, changes in the clientele, and improvements in technology, the solutions provider needs to deliver a scalable solution which can adapt to external factors without affecting analytical capabilities.
Onboarding and Upskilling: Deploying new tools into your organizations business processes can be a major change for existing employees. End-users and business teams need to be intimately involved with the technology since deployment, to ensure optimal usage of the solution without impacting the allocated budget. To avoid training every employee to carry out analytics, there should also be room for onboarding new members who can contribute equally to your analytics efforts.
Self Sufficiency: End-users should be less and less dependent on BI and IT teams for their analytics for carrying out the entire process from data discover and preparation, all the way to collaborative decision-making. The BI team should gradually shift to an overseeing capacity, where they ensure the stakeholders are using the solution efficiently and adhering to data compliance regulations. They also need to manage the scalability of the solution so that analysis does not get out of hand with excess data influx, or gets too difficult with minimal data availability.
Telling stories rather than preparing charts: Individuals in the organization should bring in marketing in insights and analytics and let their creative juices flow to tell stories with data. BI initiatives fail as they get mundane and boring as the visuals are sloppy or dry. To make it interesting and for users to adopt the BI practices it is important to get creative in telling stories with data and also have an ability to democratize the preparation and usage of it at a large scale.
Course5 Intelligence offers Discovery, an AI-driven Augmented Analytics solution, which is geared toward helping your organization adopt a cultural change towards democratization. A shift to an insights-first culture, where decision making is dependent on data analytics and business intelligence. A cutting-edge tool with a centralized search index, for efficient querying from a self-learning knowledge base. Ask questions of your data using natural language, let automation analyze your data, and access contextual insights in real-time.
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