A short guide to improving analytics adoption and transforming your organization.
A data-driven organization is an enterprise where every person is aligned towards improving a consistent set of key performance indicators (KPIs) that are recognized as important to the organization. Employees interact with data and take action to improve these KPIs, and all levels of the organization use data to support decision-making in their part of the business.
This how-to guide introduces the key steps that an organization needs to take to become data-driven. For a more detailed look at the six steps required to become a data-driven organization, read our best practice guide.
1. Implement a modern data architecture
Cumbersome manual data management processes are no longer enough to meet the needs of modern organizations. Business users need immediate access to relevant insights, and to do that, data infrastructure must be able to evolve rapidly.
The need for greater data agility has exposed two issues. First, that the old approach to data warehousing was costly and time and resource-intensive and often delivered incomplete insights. And second, first-generation approaches to big data were geared to highly technical users who would code and hand-craft complex queries.
Signs that your organization does not have a modern data architecture are the inability to handle multiple data sources and analytics use cases, the inability to scale data volumes and users, and the need for specialized resources to support complex data pipelines.
A modern data architecture is business-centric, adaptive, and flexible enough to tackle all your use cases. It should use artificial intelligence (AI) and machine learning (ML) to automate manual tasks as well as use the inherent scalability and security of cloud computing.
2. Use automation to unify data
Most organization’s applications and analytic solutions were never designed to work together, and this makes it difficult to share data and insights across organizational boundaries. Without a modern data architecture, business users can spend weeks creating simple reports off multiple systems, often with errors and inconsistencies due to a lack of technical knowledge and understanding of the data involved.
To fix this, many organizations have invested in complex data integration software, which requires teams of highly skilled data engineers to develop and maintain the data flows.
The use of AI and ML to automate data integration is set to transform this time and resource-intensive approach through the ability to automatically detect relationships between data elements, refine data, and create a semantic layer.
3. Deliver data as a service across the organization
Business user frustration with centralized and IT-managed analytics has led to a proliferation in the use of desktop data discovery tools. Although this has increased end-user autonomy, it has come at the expense of creating data silos that cause analytical inconsistencies and the potential for bad business decisions.
By delivering data as a service (DaaS), centralized IT teams can provide a shared version of the truth across the organization, as well as the autonomy to extend these centralized definitions to deal with local use cases and data sources.
Fundamental to the delivery of DaaS is a multi-tenant cloud-based business intelligence and analytics platform that enables the delivery of virtual analytics tenants. It’s the virtual nature of these that enables centralized teams to deliver a single governed data set while enabling decentralized teams to connect and enrich these sources with edge data, without impacting other groups.
To discover more about how DaaS is a core component of a modern data architecture, read our best practice guide. Further information can also be found in our white paper, governance in the age of data discovery.
4. Make analytics easy to consume
By definition, a data-driven organization should have a vision that everybody has easy access to the data they need to make decisions. This means the provision of a range of different user interfaces tailored to meet the knowledge and skill levels of the users, or embedding analytics in applications where users spend most of their day. It means that front-line workers can readily see insights and data analysts can blend their own data with enterprise data. It means data scientists can search a data lake catalog for relevant data sets, and it means that developers have flexible APIs to embed analytics into business applications.
To achieve this, an organization needs to invest in a BI and analytics platform that supports a range of information delivery options and styles, from pixel-perfect reports to responsive dashboards as well as ad-hoc data discovery and mobile devices. On top of this, the ability to create machine-generated insights using AI is becoming increasingly important to improve user productivity and uncover hidden drivers of business performance.
Embedding analytics into business applications is becoming a proven approach to significantly improving analytics value to end-users, especially non-technical users. By removing the need to shift to a separate analytical tool or application, business users can make informed decisions within the actual business process they are working on, streamlining decision-making and the ease by which they access data.
A further advantage of embedded analytics is that the business application software providers can deliver pre-built data models, dashboards, and reports, saving months of work for data-driven organizations and dramatically speeding up time to value from analytics.
5. Design analytics with business outcomes in mind
One of the greatest barriers to becoming a data-driven organization is not technology; it’s the decision-making culture within the organization. Years of experience have taught us that two things are key to creating a data-driven culture–a top-down initiative to use data to drive decision-making and having the entire organization working off the same data, metrics, and goals.
To achieve this, it is important to design analytics with business outcomes in mind, so that people focus on the most critical metrics for their role. A top-down approach leads to a set of interconnected metrics that drive the things that are most important for c-level executives.
Once the key metrics have been defined, organizations then need to identify the action points–these are the most influential business attributes that can be adjusted to improve a metric. These must be things that an organization can change, not market conditions, for example, and almost always can be described in terms of people, products (or services), and processes.
For more information on Infor’s value-based design methodology that has helped hundreds of organizations become data-driven by focusing on business outcomes, read our best practice guide on the six steps to becoming a data-driven organization.
6. Monetize your data
The final stage of becoming data-driven is when your organization starts to realize the value of sharing data outside the business with customers, suppliers, and partners. The two most common approaches involve either monetizing data to create new revenue streams or providing added-value analytics on your core product or service that differentiates your offering in the marketplace, often improving customer loyalty at the same time. It goes without saying that it is perfectly possible to combine both approaches too.
Of course, building an analytic product is probably a new concept for your organization. The good news is that there is lots of practical advice available to help, often gleaned from the data-driven organizations that have done this already. A typical analytical product requires a business case, a go-to-market strategy, development, and finally, a product launch. You can read more about this process in our how to plan, build, and launch an embedded analytics product guide.
The road to becoming a data-driven organization
As stated at the start of this how-to guide, a data-driven organization is an enterprise where every person who makes decisions has access to the data they need, when they need it.
Delivering that vision requires organizations to go through a series of steps that starts with building a solid data foundation with a modern, flexible data architecture, using the latest AI and ML technology to automate resource-intensive manual data management processes. Delivering data-as-a-service across the business provides that elusive single version of the truth while accommodating decentralized teams and their edge data.
Once the data is in place, organizations need to ensure they have the right interfaces for all of its users, regardless of their technical skills and data knowledge. The dashboards and reports used need to be focused on the most important business metrics, and a top-down culture of data-based decision making is enabled at every level. Finally, data-driven organizations monetize the full value of its data by securely sharing it outside the business.