Business Intelligence Best Practices: Eliminating Barriers to Insight-Driven Industries

• Business Intelligence

These are great times for business intelligence (BI) and analytics practice teams. We have never seen so many variables aligned to enable the opportunity for data and insight-driven business. There are more data sources, greater computing power, cheaper storage, better information management, and more intuitive analytical technologies. Yet, in spite of available technologies and practices, many BI implementations do not deliver the desired or anticipated results. Organizations and partners of all sizes suffer from countless oversights and poor judgment calls during planning, technology selection, and roll-out plans.

This article discusses certain business intelligence best practices which every company can follow. We have also tried to do a deep dive into some mistakes to avoid to be able to launch scalable, cost-effective and next-generation BI and analytics best practices.

Best Practices: Course5 Perspective

Invest for BI across Business Functions

Your enterprise cannot experience true value from analytics if your BI practices are disconnected and not adopted company-wide. Only when every business unit is leveraging BI to optimize their function, will the entire company experience BI success and maximum ROI. It is also important to invest in IT support early. As we are seeing these days, the CIO and CTO of the organization have a major say when it comes to any BI and analytics practice.

At Course5 Intelligence, some of our leading tech and CPG customers have been able to drive business decisions with actionable data and insights – which initially came from first-level BI dashboards.

Related read: Framework for modernization of insights

Set up a BI Blueprint

Before you deploy your BI tools and engage in analytics activities, it is essential that you map out the scope of the project. Once you have laid out the objectives and the requirements, it becomes easier to develop an effective strategy for conducting analytics and gathering insights.

An organized and precise strategy ensures that both individuals and teams involved in the initiative, are aware of their exact role and their area of contribution. This is the groundwork on which the analysis will be carried out and the BI formed.

Your analytics roadmap also needs to establish your data integration models, for seamless analysis of unstructured data. Work with stakeholders to manage their expectations, and identify the KPIs that are crucial for tracking performance. Set up a standard for technology and reporting requirements, accounting for the scalability of the solution as well.

Have Well-defined Targets

Just a blueprint will not aid your analytics requirements, if you don’t have clearly set targets for achieving on an individual or team basis. Consider the requirements from each team, and delegate tasks according to capabilities and available datasets.

Having targets to meet, will enable also you to measure performance based on how much of the target has been met. Mapped out KPIs and relevant metrics are crucial to gauging progress and achievement to targets.

A targeted approach also helps to breakdown your data according to importance and validity. You can also schedule meetings and interviews with relevant stakeholders, who will be the ones working with the insights post data analysis, and are responsible for the decision-making.

Identify Crucial Data Sets

Your organization like most collects data from every business function. But the data is usually stored across different software and servers. And analyzing data across Excel sheets and ERP systems, SQL servers and HTML pages is a daunting task. Just remember to start small. Let your targets determine which data needs to be analyzed, identify the source, and get started. In the end, you may take advantage of an enterprise data warehouse to improve performance. In our experience, we have seen our customers and partners start small and then aim for the larger picture.

Remember – we will all get there but the journey starts with something small and integrating clickstream data with sales data (first small step for analytics universe!)

Establish a Data Governance Framework

To safeguard your enterprise, your employees, and your data, you need to develop a robust data governance framework, in compliance with all necessary regulations. Data governance covers data security policies, decision-making rights, data accountability, and processes to enforce the regulations on both teams and individuals handling the data.

Having a well-defined data governance process will allow you to safely consume high-quality data, streamline management, and minimize operational spend. It will also allow you to optimize the decision-making process, improve operational efficiency, and deliver best-in-class BI.

Develop Impactful Dashboards

The element which makes modern business intelligence and BI tools stand out, is the use of dashboards to visualize the data. You need to consider four factors when designing your BI dashboard. It needs to be simple, should have clarity in viewing the data, the insights should be easily conveyed, and the tool should be consistent across projects.

The data itself and the analysis of the same may be complex. But the dashboard should remove all technical hurdles, with Augmented Analytics capabilities automating analysis and offering contextual insights. The most relevant insights should be presented to the end-user of the tool, and not inundate their screen with irrelevant data.

This enables decision-makers to understand the data through simple interactive images and insights, without needing the requisite analytical skills. Apart from making BI easily consumable, an efficient dashboard should also possess high-end querying capabilities, for rapid access to curated insights. End users can track metrics they are interested in, while filtering searches and settings to fit their specific requirements.

Users can also customize their tool for reports with alerts, notifying them of significant change in the data they are tracking. The alerts can also be triggered when immediate action is required, and decision-makers can adjust strategies in real-time, to account for changes within the market.

The BI tool can also possess Embedded Analytics capabilities, allowing users to embed reports and dashboards into other tools that the team maybe using. This fosters easy sharing of crucial BI, and promotes collaboration between business functions.

Start Small

Yes, every organization has lots of business questions that need to be answered on day 1. Is that really needed? Million-dollar question. When analytics teams and partners start compiling individual business questions and needs, the sheer size of the data required to be moved can be overwhelming. This is only compounded when every employee starts clamoring for data immediately. We recommend starting with a small list of crucial and mission-critical data questions. First try answering the questions with available data, and if that does not work, find out other departments that might have that data. If that fails as well, the time is ripe to explore data pipelines that need to be brought into the gambit of existing data sets – sales, marketing, transaction, clickstream, etc. However, do not try to do everything in one go, build small project plans and execute towards it. Gather requirements and add input, build, test, and repeat!

Develop a Data Culture

While setting up a new plan for BI, the value of data has to be glorified to get the entire enterprise on board. And only when all business functions are operating in collaboration towards a shared objective, a robust data culture is created. This makes the data meaningful, the BI valuable, and the resulting business decisions to be strategic.

You may experience some roadblocks if departments are unwilling to adopt new techniques and technologies. For some of the users, new platforms and processes might be overwhelming. But they have to realize that every team can benefit from these tools, and the benefits overshadow the costs.

Identify a platform or solution which is simpler to use with a friendly back end and with minimal requirement to code. In this age of tableaus, DOMOs and Power BI solutions, understanding insights is simpler and it’s fairly easy to access training materials.

Find a Data Champion

Organizations across industries are moving towards data-driven culture. When we partner with our customers, we try to identify one or two data champions to work with. It helps to partner with an internal data champion as they understand the importance of driving initiatives based on data. It helps organizations and internal teams to align with the need to drive insight-driven action and not heuristics.

Empower Users with Storytelling Capabilities

Everyone within and associated with your business can benefit from contextually related data and insights. With higher adoption of insight-driven business, better business management systems are available for organizations to build on.

In the end, organizations want compelling storytelling capabilities, as it is a highly effective way to help business people understand data and insights. Data visualization and data storytelling are key to not just understanding the insights, but being able to present the findings, and drawing up a plan of action. Training teams on the right skillsets and providing high-quality templates are essential elements for a successful BI strategy in any organization.

Now, let’s look at some potential mistakes to avoid as we embark on the data and analytics journey.

Potential Pitfalls to Avoid

# 1 Creating data silos and multiple versions of truth

A few years ago, data discovery tools caught the eye of businesspeople who believed that self-service was their new information utopia. They were able to employ user-friendly tools that enabled the uploading and manipulation of personal data, followed by the visual exploration of their custom data models.

Several challenges have emerged over time, and it usually takes two to three years of actual usage to realize and identify them:

• • Silos
    Analytics silos are typically formed by self-service users who prepare their own data, create reports and charts, and share them within their work group. They tend to exist in a bubble, creating their own library of analytical content, with their own calculations and preferences. These silos can emerge in other parts of the organization that employ self-service tools, resulting in multiple analytical groups and multiple versions of the truth.
  • Scaling
    Self-service and data discovery often start life in a department or smallish work group, where a business leader has elected to invest in a subscription for his/her team. Adding more users who need resources, security, and more data sources, can easily create a scalability problem. Self-service and data discovery tools do not inherently possess all the underlying data, content, administration, performance, user, or security management that is critical to support larger user audiences and more complex needs.

# 2 Great-looking BI platform but low adoption leads to shutdown

Adoption rates for BI and analytics remain alarmingly low, hovering at around 30 percent according to Gartner’s Cindi Howson. Many organizations have yet to reap the benefits of their investments because they just can’t get their users on board. We have struggled with similar situations in the past with our partners, but timeline education and constant roadshow have helped bridge that gap and improve the 30% rate to more than 60%.

# 3 Failure to monetize data with a pay-out model

This is a new concept in the industry and many analytics organizations don’t realize the power of their own analytics solutions. All the efforts, pain and sleepless nights can be easily monetized by building a pay-out model. Once a scalable solution is in place, use the right methodologies to charge back internal stakeholders on all analytics initiatives. Assigning a $$ value to analytics initiatives can help drive the right adoption as well as show the true value of data to larger parts of the organization.

All of us have the recipes to drive analytics success but the Business Intelligence best practices discussed above are key to the success of any analytics initiative. We have seen multiple companies launch analytics programs, but only a handful have been able to sustain the effort for longer periods of time.

The evolution of cutting-edge BI tools is revolutionizing analytics, making it a lot more accessible, and increasingly enabling businesses to be data-driven.

Course5 Discovery is a cloud-based AI-driven Augmented Analytics solution, that is more than capable of meeting your BI requirements. Supporting multiple personas, and seamlessly integrating data from multiple sources, it offers you relevant and actionable insights in real-time. Get in touch with us for automating your analytics initiatives, fostering a culture of data-based decision-making, and monetizing your enterprise data.

Author: Bhaskar Dey
Contributions by: Praveenkumar Sathyadev

Note: This article was recently updated to account for business intelligence best practices driving analytics success.