Building An Analytics Culture In Your Company
Establishing an analytics-driven culture within a business presents something of a “chicken or egg” dilemma. Are short-term objectives key to finding value in business intelligence (BI), or are long-term goals most critical to a successful strategy? In developing BI projects, is it more important to find the data to answer key questions, or start with the data to uncover the right insights? Perhaps most important, what is the source of creativity, and how does it inspire execution? Is a top-down or bottom-up approach more likely to succeed?
In most businesses today, BI is required, and analytics happens anywhere within an organization. The IT department typically manages data aggregation and BI, generally focusing on specific internal clients with external customers in mind. However, the impact of the efforts may not be realized corporate-wide. Individual employees may use end-user tools for business analytics that serve the needs of their team or department. Often, midlevel operational units that roll up from department to division and division to company are ignored, leading to missed opportunities for organizational impact or change.
This neglect of the operational units is both the symptom and the cause of failures to disseminate analytics capabilities throughout an organization. It is the chief obstacle to successfully rallying employees in the consistent and systematic execution of corporate goals.
Empowering Analytics Across The Business
The challenge in driving data-driven intelligence into different end user communities often lies in a lack of data literacy. There is a sound business case for BI and the development of systems and processes that can draw from many sources to present clean and trusted data. These practices are critical to enabling analytics within business units. To come full circle, the organization must not only present data to a broader audience, but ensure that everyone understands it and can leverage enterprise-relevant data for value in their business unit.
The right tools help put data at the fingertips of everyone in the organization. Even simple functionality, such as a narrative description of what’s on a chart, can help to simplify presentation details, exposing the most important parts. A narrative box might explain if total sales are part of a particular report, what the average sales are per location and how much above or below the norm different results represent. These points can guide the end user to understand the “why” behind the situation illustrated by a report or dashboard.
Another approach is to use state-of-the-art reporting designed for ready access to key categories of information. Infographics can be customized to deliver relevant data points to specific recipients. With these kinds of techniques, organizations can begin to complete the circle. The use of data will explode as a natural integration into the daily life of all employees engaged in any work, making data consumable in the format most relevant to the needs or skill of each individual user.
Implementing Analytics Processes
Mature organizations must address gaps in the use of analytics by operationalizing data. An increasingly important piece of this process involves analytics automation — for example, the use of AI to automate and simplify decision-making. Centralized oversight into the BI architecture by analytics experts can unpack data assumptions. Usage must come into play to ensure data is answering the right question and delivering the correct answer. Similarly, data security and privacy requirements must exist to protect corporate interests.
Centralized oversight is not necessarily the same thing as centralized processes. Leadership can provide oversight by investing in the data management solutions and information architectures needed to establish a foundation of trust in the data. One best practice is to contribute to this by creating a center of excellence (COE). The COE team can provide leadership, best practices, research, support and/or training. It can be staffed with analytics professionals who understand governance and can share lessons learned in earlier projects. But regardless of the foundational central resources empowering users to do more with data, analytics initiatives do not all start at corporate IT and the executive levels.
Conjuring ‘Data Magic’
In some cases, mistrust of analytics as a key decision input can act as a counterweight to the establishment of evidence-based processes. Here, a delicate balance comes into play: encouraging individual data creativity while establishing guidelines and the business philosophy for data utilization. This builds organizationwide momentum for data, one department (or even manager) at a time. It confronts holdouts adhering to the “I don’t need data; I have experience” mentality with hard evidence demonstrating the value of analytics. The magic happens when gut feel and data come together.
With frameworks in place and less overt corporate oversight surrounding data, an explosion of analytics can occur. This sort of evolution is a fundamental pivot point in the relationship between an organization and its data. Individuals can recognize the most useful links, and they can begin to seek these links once there is acceptance of change. In our knowledge-based economy, the viability and life span of an organization depend on the ability to identify and address this reality through analytics.
For executive leadership, one simple metric will help demonstrate the organization’s willingness and ability to adapt to change as a fact of competitive operation: utilization. Organizations will see increased use of business intelligence tools as employees achieve success accessing the data they need to make better decisions.
Organizations need to reach a 60% to 70% data utilization rate to know they have the right aggregation of data. At that rate, organizations should have confidence that the data has integrity and that users have the right amount of access to the data. By bringing their individual creativity, as well as their own roles and priorities to the data, the users can harness what they need in a format that makes it digestible. Even people who just need to look at the analytics outputs can incorporate them into their daily work routine. Success will come when analytics becomes just the way of life in the organization.
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