David Menninger's Analyst Perspectives

MicroStrategy is a long-standing business intelligence and analytics vendor that operates worldwide. Founded in 1989, this publicly traded company with hundreds of millions of dollars in revenue recently held its first in-person conference since prior to the pandemic. Similar to previous in-person events, the event was well attended by about 2,000 attendees and exhibitors. The theme, “MicroStrategy One,” is a way to explain the breadth of capabilities the company offers. The breadth of the product offering is one of the company’s greatest strengths, but also one of its biggest challenges.

Organizations are becoming increasingly aware of the potential value that can be gained by processing big data. As data sources grow, it becomes important to have tools and methods to effectively process, analyze and visualize this information from disparate systems and warehouses.

Data analytics provide valuable insights and enable organizations to make better decisions, improve performance and gain a competitive advantage in the marketplace. Analytics can change frequently depending on the data being analyzed and the methods used to gather and process it. Factors such as new data, changes in the underlying systems or updates to algorithms can all contribute to differences in an analysis. AnalyticOps helps ensure data is accurate, up-to-date and consistent across different systems and teams, and that analytical models are robust, reliable and continuously improved.

Markets have been more volatile than ever. It creates a need for decision makers to utilize technologies such as artificial intelligence and machine learning (AI/ML) to better understand the external factors that impact their business. By identifying these factors, organizations can better plan for changing market environments and seize market opportunities. However, manual modeling is a time-consuming process and results in a limited number of models and tests. Also, updating those models is slow and laborious. With the addition of market volatility, it creates multiple challenges for CFOs, managers and financial planning specialists. With limited exposure to external drivers of demand and delivery, the process becomes very costly. Developing accurate forecasts requires integrating exogenous data with the internal performance data, but it’s challenging to find quality external data and then get that raw data clean enough to input into any model. My colleague, Robert Kugel, recently shared his perspective on using external data for forecasting, budgeting and planning to enhance predictive capabilities.

Ventana Research recently announced its 2023 Market Agenda for Analytics, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value from technology investments to improve business outcomes.

Organizations conduct data analysis in many ways. The process can include multiple spreadsheets, applications, desktop tools, disparate data systems, data warehouses and analytics solutions. This creates difficulties for management to provide and maintain updated information across multiple departments. Our Analytics and Data Benchmark Research shows that organizations face a variety of challenges with analytics and business intelligence. One-third of participants find it difficult to integrate analytics and BI with other business processes. Participants also find that not all software is flexible enough for the constantly changing business environment, and that it is hard to access all data sources.

Embedded business intelligence (BI) continues to transform the business landscape, enabling organizations to quickly interpret data and convert it into actionable insights. It allows organizations to extract information in real time and answer wide-ranging business questions. Embedding analytics helps tackle the issue of extracting information from data which is a time-consuming process. Our research shows organizations spend more time cleaning and optimizing data for analysis rather than creating insights. On top of that, they are adding more data sources and information systems which in turn introduces more complexity. Our Analytics and Data Benchmark Research shows that organizations face various challenges with analytics and BI. More than one-third of participants (35%) responded that they find it hard to integrate analytics and BI with business processes and connect to multiple data sources. By embedding analytics and BI into business processes and workflows, organizations can enable users to make critical decisions fast, enhancing overall business agility.

In today’s data-driven world, organizations need real-time access to up-to-date, high-quality data and analysis to keep pace with changing market dynamics and make better strategic decisions. By mining meaningful insights from enterprise data quickly, they gain a competitive advantage in the market. Yet, organizations face a multitude of challenges when transitioning into an analytics-driven enterprise. Our Analytics and Data Benchmark Research shows that more than one-quarter of organizations find it challenging to access data sources and integrate data and analytics in business processes. Vendors such as IBM offer a broad set of analytics tools with self-service capabilities that allows organizations to reduce IT dependencies and enables decision-makers to recognize performance gaps, market trends and new revenue opportunities. Its technology can simplify data access for self-service applications, enabling users to make business decisions informed by insights and take the guesswork out of decision-making.

If you’ve ever been to London, you are probably familiar with the announcements on the London Underground to “mind the gap” between the trains and the platform. I suggest we also need to mind the gap between data and analytics. These worlds are often disconnected in organizations and, as a result, it limits their effectiveness and agility.

Organizations are managing and analyzing large datasets every day, identifying patterns and generating insights to inform decisions. This can provide numerous benefits for an organization, such as improved operational efficiency, cost optimization, fraud detection, competitive advantage and enhanced business processes. By bringing the right, actionable data to the right user, organizations can potentially speed up processes and make more effective operational decisions.