Integrated analytics is a term we use to describe a next generation business intelligence platform and methodology. In our view, it is a new digital operating model to run the enterprise. In today’s post, we explain the key differences between integrated analytics and existing solutions such as Microsoft BI, Tableau, Qlik, and others.
After working with the data of global fortune 500 brands and getting a deep understanding of the key issues that impact having an integrated view that facilitates a data driven culture, there are two main challenges that set back many organizations still today.
Two key data & analytics challenges for the enterprise
In today’s fast-paced business environment, leaders need a broad, deep and connected view of their enterprise to have a true understanding of what is really happening in the organization. This is particularly true in a digital enterprise since cross-functional collaboration is key to success. However, despite data lake projects, and existing BI tools, many organizations continue to face two major challenges:
- Highly fragmented information across business functions and units
Key reports with curated data are in Excel and PDF files in silos.
More granular information and analytics are in multiple departmental solutions and locations making it difficult to connect the dots with other data points across the organization.
Business units often build their own version of analytics for each department thereby further increasing complexity and cost while limiting the capacity for collaborative problem solving and viewing the pieces of the puzzle as a whole.
- Long cycle-time to build multi-domain analytics solutions
Business and functional experts see value in connecting information across domains of expertise and business units. However, they are disappointed with implementation timelines and costs – often months or more than a year. The cost of doing nothing is higher and ultimately results in a competitive disadvantage.
Closing these two gaps can result in significant value creation and information advantage
Again, speaking from personal experience, I have seen these challenges over and over and wanted to create a resource to simplify the process of connecting information broadly while making it streamlined and not complicated to execute.
Why integrate analytics? To improve organizational effectiveness – Integrating information across domains of expertise and business units is central to creating a data-driven organization. Executives and the board can be empowered to gain access to insights such as seeing patterns, costs, opportunities, and challenges at a holistic level. The resulting benefits include:
- A 360 degree view of the enterprise – Selected data and analytics solutions are delivered in one application to create a digital operating model. For example, executives have access to an interactive view of financials, customers, sales, risk and compliance, operations, information technology and external environment factors such as competitors, the economy and financial markets. Likewise, leaders and teams in sales, technology, business operations, risk, have their version of a digital operating model, utilizing the same data, at a more granular level.
- Visibility, transparency and improved cross-functional collaboration – The above digital operating model is used to create a data-driven organization. Specifically, the goal is to improve organizational alignment with clear performance objectives cascading from the C-suite to business units and functions and their sub-teams. In addition to performance objectives, enterprise capabilities are also modeled. For example, people, processes, technology, suppliers, and facilities. This integrated model of performance and capabilities enables teams to not only identify performance gaps but also to connect the dots to specific causal factors such as how do account balances, product margins and mix impact growth and profitability; or how do business processes and information technology impact enterprise efficiency?
- Simplicity – Organized and connected information yields superior insights. In contrast, highly fragmented information makes it difficult to understand enterprise performance drivers and quickly focus on improvement levers.
- Speed and agility – Organized and connected information makes it possible for teams to rapidly improve analytics solutions and create new solutions by adding modular information and analytics building blocks. This enables teams to gain an information edge.
What is the difference between integrated analytics and traditional business intelligence tools?
In three words, the key difference is the semantic data model that offers expanded capabilities that standard BI tools do not provide. This type of data model is the core of enterprise analytics as it enables enterprises to organize information and derive value from it quickly and comprehensively. CHORAL integrated analytics is engineered to rapidly build and improve semantic data models along with analytics solutions. What that means in laymen terms is extra horsepower in connecting your data.
The model is flexible, extensible and is integrated with:
- Software services to automatically build, visualize and manage the semantic data model.
- A library of analytics solution (domain-specific and multi-domain) built on this data model. These solutions may be rapidly adapted to client-specific needs.
- Simple authoring and data visualization tools which are integrated with the semantic model to enable non-IT experts to author analytics solutions and data storytelling.
The result is:
- Integrated analytics solutions which are modular and inter-operable, and
- Faster solution implementation and improvement.
The combination of speed and integrated information results in information advantage.
In contrast, business intelligence tools focus on proving the software to assemble your analytics solutions. For example, a summary description of Microsoft Power BI is:
“Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Your data may be an Excel spreadsheet, or a collection of cloud-based and on-premises hybrid data warehouses. Power BI lets you easily connect to your data sources, visualize and discover what’s important, and share that with anyone or everyone you want.”
In our experience, the ability to rapidly build and update a semantic data model is critical in delivering great analytics solutions that provide comprehensive value. BI tools currently do not provide the data model components, associated software services and methodology to rapidly build an enterprise semantic data model. This is a key improvement opportunity — to deliver speed, simplicity, efficiency, and integrated information yielding superior insights.
In conclusion, integrated analytics offers a new type of data modelling that is comprehensive, easy to set up, and provides additional capabilities with less upfront effort to build a custom solution. Interested in learning more? Contact us to set up a demo.