In business intelligence, there’s an unusual paradox in BI software today. The market is projected to grow at a rapid pace for the next four years, but is currently lingering. Traditional factors that often lead to BI adoption success are starting to lose influence.
Today, relying on a VP or C-level executive to champion a BI initiative to success isn’t enough. What’s needed is a more coordinated approach across organizational, process and technological areas of a company’s BI implementation roadmap strategy to get the greatest value possible. To increase the probability of success for business intelligence adoption in an organization, it’s important to take into account a series of factors found from an organizational, process and technological perspective.
- By the end of 2020, the Global revenue in the BI and analytics software market is forecasted to grow to $22.8B, according to Gartner.
- The global BI software market accounted for $16.3B in 2015, and is expected to reach $26.5B by 2021, growing at a compound annual growth rate (CAGR) of 8.4 percent between 2016 and 2021, according to Zion Research.
- Big Data and business analytics software worldwide revenues will grow from nearly $122B in 2015 to more than $187B in 2019, an increase of more than 50 percent over the five-year forecast period.
- BI adoption is lingering at 30% in the majority of enterprises, according to Gartner
A lack of BI adoption is slowing down organizations of all sizes from being able to make the most of the massive amount of data they’re producing daily. BI applications and tools enable greater insight and intelligence into areas of business models never analyzed or understood.
Of the many areas BI is contributing to today, one of the most valuable is gaining greater insights into customer behavior by predicting buying outcomes. By knowing more about their customers, businesses can streamline their business processes and make them more efficient. When this occurs, they excel at improving product quality and customer service.
Data is proliferating in each of the six areas shown in the graphic below. Data scientists, cloud-based applications and the Internet of Things (IoT) are among the most prolific sources of data today. The combination of these factors is driving a data onslaught in every organization. BI applications and tools need to gain greater adoption for companies to make the most of the valuable data being generated from each of these sources.
Facing The Challenges Of BI Adoption
The problem of increasing BI adoption is a multidimensional one with no quick fix. Ideally, there needs to be a balance and alignment of the organizational, process and technological factors in order for business intelligence adoption to succeed. Galvanizing all three of these core areas needs to be a central business focus that everyone can identify with and take an active part in accomplishing. This is where the majority of business intelligence adoption strategies fail.
There’s often no unifying business purpose that benefits everyone; it’s just not how the world works. But when every employee using the system has a strong sense of ownership and purpose, all the strategic areas needed for the improvement of BI adoption can fit into place, increasing adoption rates. A senior management champion alone can’t galvanize the purpose of a BI system as powerfully as a shared goal and a desire on the part of employees to excel with the new BI system.
Overcoming Technology-Related Challenges To BI Adoption
The leading technological factor that’s slowing down BI adoption is the lack of integration with legacy and 3rd-party databases, as well as the many enterprise systems that provide greater contextual data. BI applications running on a single database have limited potential to deliver contributions to organizations. The greatest technological inhibitor to business intelligence adoption is non-integrated BI and analytics tools in which users are manually importing data in an attempt to get greater insights.
Many organizations begin their BI integration strategy by concentrating on legacy and 3rd-party databases first before moving on to larger, more complex integrations. These integrations involve enterprise applications like customer relationship management (CRM) and enterprise resource planning (ERP). Integrating with legacy and 3rd-party databases often requires customizing a connector or adapter, which could translate into professional services fees and other additional costs.
For organizations with in-house IT teams, implementation is a relatively straightforward process. It costs time, and time is often what many IT teams are very short on as they try to support groups of users across a larger organization. Fortunately, many analytics and BI applications provide advanced adapters for integrating with CRM, ERP and other enterprise apps. It’s less expensive to purchase an adapter or connector created by the BI provider than it is to pay a system integrator to complete a custom integration from a BI app to an enterprise system that’s already implemented and running.
Every leadership team grapples with balancing the costs of integration versus the goal of providing real-time analytics and BI access company-wide. The following graphic provides an overview of the stages that organizations go through as they integrate BI applications into their IT systems and workflows.
This is a critical phase of integration, as it provides customer-driven data from the CRM system, along with a wealth of transactional data from the ERP system and its supporting apps. Organizations reaching the highest levels of strategic business intelligence adoption can integrate all of these systems together, attaining real-time analytics and reporting enterprise-wide.
The ascension of BI adoption from only integrating with legacy and 3rd-party applications to integrating with enterprise apps is critically important for accelerating BI adoption. Without the added data from enterprise applications, BI adoption tends to stall, stop and eventually decline. From this standpoint, it’s a fair assumption to say that if any company wants to gain high BI adoption levels, it’ll need to integrate with CRM, ERP and other enterprise systems that are core to their daily functions as a business.
Factors Driving Greater BI Adoption
Real-time integration between BI systems and legacy/3rd-party databases, CRM and ERP systems
BI projects that attain the highest levels of adoption focus on these areas first and start building out a roadmap of integration points to guide development. It’s critical for timeframes to be communicated company-wide regarding integration to databases and apps, as that provides other departments visibility into when they need to begin their part of the BI implementation project. In larger organizations, the project management office (PMO) manages the business intelligence roadmap, and a senior executive takes ownership of the responsibility. If an organization doesn’t have a PMO, the best approach is to define a project leader in its headquarters who can manage the BI roadmap strategy to completion on a daily basis.
Defining and acting on data quality standards early and often during the BI implementation phase
Data quality can make or break any BI implementation, as users will immediately judge the value of any BI system by the results it generates when they first use it. Making data quality a priority pays, and it helps accelerate software adoption when users see accurate reporting and analysis that reflects the actual conditions of the company.
Selecting a flexible, modular system that can scale with your user’s needs is a must-have to drive BI adoption
BI adoption increases when a system can flex and respond to the needs of a broad base of users without forcing them to change how they work. The more modular and agile a BI system is, including the flexibility for defining custom workflows by business analysts, the greater the level of adoption will be.
The ability to customize dashboards and reports, generate advanced data visualizations, and enable more responsive self-service are critical success factors for driving BI adoption
These are must-have features in any BI application in order to drive greater adoption. Across the spectrum of small and medium businesses (SMBs) to enterprises, these four areas are the foundational features of applications that drive adoption. Companies that excel in these dimensions of BI include Microsoft, MicroStrategy, Tableau and Yellowfin. The following graphic provides an overview of technology priorities by organization size. It’s a part of a broader study by Dresner Advisory Services summarized in the Forbes post, Small Businesses Are The Real MVPs Of Analytics And BI Growth.
Selecting a BI application that delivers excellent customer experience and intuitive, easy-to-use, streamlined workflows are essential
BI applications continue to improve in this area of product design. Today’s leaders include Birst, ClearStory Data, MicroStrategy, Microsoft, Oracle, Qlik, Salesforce, TIBCO and ZoomData. Based on the research by Dresner Advisory Services, Gartner and others, it’s clear that this is a future product direction of all BI vendors in the market today. Selecting a vendor that excels in this dimension will drive greater BI adoption when the implementation takes into account the other factors mentioned.
BI Roadmaps Bring Technology Key Success Factors Together
Bringing the five technology success factors together into a unified business intelligence roadmap helps everyone in an organization visualize what success looks like and helps it move faster towards that success. Every BI vendor has a product roadmap available for each product line, and several have roadmaps defining their product direction by vertical market. Making sense of all the vendor roadmaps requires organizations aiming for high BI adoption to create their own.
Defining a BI Implementation strategy defines which legacy, 3rd-party databases and enterprise systems will be integrated. It also provides an assessment of how BI will be used. Efforts to implement business intelligence are often first focused on customer-driven advanced analytics and the creation of role-based dashboards.
As BI adoption grows over time, greater insights can be gained from manufacturing, logistics and supply chain systems leading to a new base of knowledge in the company and manufacturing intelligence. Predictive analytics-based efforts shown on the right side of the following figure are often the catalyst that leads to greater operational and manufacturing performance.
Five Strategies For Increasing BI Adoption
Concentrating on technology-related success factors sets the foundation for enabling greater process and organizational change. Key success factors, from a process standpoint, include clearly defining the business problem/processes and gaining consensus on what problems the BI system needs to solve. Second, processes need to be defined by user expectations, using an audit of their needs. Third, there needs to be process workflows that allow for the BI application and components to align with your user’s specific needs.
Change management plans and frameworks often take these process-based key success factors into account when defining an overall business intelligence implementation strategy. Organizational success factors include having an adequate budget defined before the project begins, support from senior management, having a dedicated BI project manager in place, a scalable team supporting that manager, a clear plan and a dedicated implementation specialist from the provider of the BI application.
Taking the technology, process and organizational success factors into account, here are the top five strategies for increasing BI adoption:
A clear, well-defined BI business case that gives every participating employee a chance to see how their contribution drives success
Providing the opportunity for greater autonomy, mastery and purpose for every employee is the cornerstone to making BI adoption rates improve. The greatest BI implementations aren’t pushed to high adoption levels; employees drive them there.
Selecting a BI application with a flexible, agile architecture that can flex to changing requirements and needs, including supporting embedded analytics
The five technology success factors address the issue of having an agile, flexible BI application that can scale. Flexing across analysis and content creation, data management, infrastructure, and embedded analytics are all essential to set the foundation for businesses to implement business intelligence and grow.
An experienced management team that includes directors, vice presidents and C-level executives who can cut through the cross-functional confusion and get things done
Contrary to the popular belief that it only takes a senior-level management champion, experience has shown that cross-functional teams will often resist change. It often takes a unified effort on the part of senior management to get BI software implementations done; all must be in favor and actively support the effort to break down barriers.
Ensuring data quality from the very beginning of the project is a must-have
Oftentimes, data quality is relegated to the last of a series of factors that companies look at when planning and developing their BI implementation. Data quality needs to be designed in from the very beginning in order to get the maximum results possible, while ensuring that the BI applications being launched deliver data that users can take action on.
A business results-driven development approach needs to underscore all efforts
Always tying back to business factors and the urgency to gain greater insights that can be turned into revenue, emphasizing business results can keep the intensity and focus at a high level until a BI project is completed. Keeping the intensity level up and focused on how BI adoption can drive revenue helps maintain it as a priority until it’s complete.
Today, organizations are facing the many challenges of improving BI adoption, and oftentimes they only get a fraction of the data they could from their systems. These five strategies for increasing business intelligence adoption help create a unified, cohesive strategy. Then, the urgency of gaining greater revenue based on insights can help fuel greater adoption.
Providing users with greater autonomy, mastery and purpose, as well as seeing how their contributions matter, also helps. The bottom line is that BI systems are designed to flex to evolving requirements more than ever before, so taking a customer- and revenue-driven approach to defining its role improves adoption rate.
What obstacles are preventing your business from implementing business intelligence software? How did you surpass them? Let us know in the comments!