In today’s digital economy, data & analytics are a strategic capability and growing necessity for enterprises to drive business value that includes: revenue growth, productivity, improved risk management and ultimately a competitive advantage. C-level executives can play a key role in leading a data & analytics transformation.
Where to Begin
At first glance, this may appear to be a challenge for non-data experts, however, business and functional leaders can take a few simple steps which will help them guide their organization to create value with data & analytics:
- Get familiar with a few key concepts – Data governance, data aggregation and analytics solutions are the 3 building blocks of enterprise analytics. These are described below. Once you are familiar with each, you can ask more specific questions to your teams to ensure that the strategic focus is correct, skills and capabilities are in place for each, execution is on track and value is being created.
- Ask questions to ensure focus on business value – There are numerous tradeoffs to consider when investing in data & analytics. Unfortunately, there are situations where enterprises have made significant investments in this area without seeing the expected returns. The C-Suite should ask questions such as:
- Do we have a robust foundation for data & analytics including: data governance, data aggregation and skills?
- How are our investments divided between building a foundation (governance and aggregation) versus delivering analytics solutions?
- What business value are we creating? Can we accelerate business value by building solutions in parallel with our data foundation?
- Are our data aggregation efforts being guided by clear business priorities which will result in data being used frequently?
- Do we have a framework to make the right tradeoffs in our analytics solutions investments?
3. Become users of analytics solutions – The C-Suite can lead analytics by example. If you are still using spreadsheets and PDF files to obtain the information you need to manage, you may want to consider the opportunity that analytics creates for management teams to thrive. Imagine having a digital operating model on a tablet with all the key information you need to lead your business. Not just a static picture with data in silos but a dynamic set of views that are connected, to allow you to answer questions immediately as you solve problems, versus having teams follow up at a subsequent meeting. A digital operating model can be transformational since it enables a data-driven organization. Things are already heading in this direction so the last thing you want is to be at a disadvantage in this area. Leaders and teams can have a common mental model of the enterprise with supporting analytics to enable collaborative problem-solving at an accelerated pace.
Data & Analytics – Key concepts
As shown in the figure below, data & analytics capabilities may be subdivided into 3 categories: data governance, data aggregation and analytics solutions.
- Data governance – ensures that there is clear ownership and accountability for data across the enterprise. This is critical to ensure that data is curated, and that access is provided to authorized personnel as needed. Robust data governance processes should be implemented to manage data through its lifecycle. This ensures accurate data and protects the enterprise from cybersecurity threats.
- Data aggregation – is the process of ingesting, normalizing, and curating data to make it available for consumption by people and systems. Key components include:
- Large scale data storage and high performance computing – the infrastructure needed to store, manage and process data.
- Reference data – such as customer information file, product catalog, market data, industry codes, occupation codes. This data is important as it is used to normalize information from multiple sources.
- Metadata hub – to create a data glossary and track data quality and lineage.
- Curated data sets – data which has been reviewed and validated by accountable owners.
- Analytics solutions – A wide range of analytic techniques can be used and solutions may be applied across the entire enterprise as shown in this model below.
- Given the number of possibilities, it is useful to create a multi-dimensional view of analytics solutions. Here’s what we recommend:
- By organization – which organization owns this solution?
- By business capability / process – where is this solution being used?
- By analytic method – for example: business intelligence, computational / statistical, robotics, machine learning, simulation.
- By purpose –
- Business process automation
- Risk management: credit risk, market risk, operational risk
- Business management
- Customer service & experience
- Growth: Pricing and sales, E.g.: leads management and the next best offer that will drive conversion
- Financial crimes: fraud management, cybersecurity, anti-money laundering
- Regulatory compliance
Why the time is now to build a digital operating model to lead your enterprise
The C-Suite stewards all aspects of the enterprise and therefore engages in problem-solving across a wide range of issues. In many enterprises, this process is supported by documents and spreadsheets which are prepared ahead of C-Suite meetings. Some challenges with this approach:
- Issues are raised and the facts to support collaborative problem solving are not available immediately. A meeting action item is assigned to a sub-team and a summary is provided at the next meeting. The lack of access to a broad, deep and connected fact base inhibits the C-Team from reaching its full potential.
- C-suite executives are problem solvers who know their business very well. Evenings and weekends are often spent reflecting on issues and seeking answers to questions which require a deep fact base to answer. Imagine C-Suite executives being able to answer questions on their own, seeing the facts, unfiltered, connected, to gain insights and probe more precisely as they meet with their teams.
The opportunity is, therefore:
- To empower the C-Suite, leaders and teams across the enterprise with a digital operating model – a broad, deep and connected view of the enterprise, thereby enabling them to reach their full potential in collaborative problem solving – to become a data-driven organization.
How can this be done? Here’s where to start.
Given the wide range of domains to be included, the operating model is built in phases.
Domains of expertise are defined, and a team is assigned to each domain to be implemented.
Solution examples, a common data model, and authoring tools can be used to enable rapid implementation. For each domain, a team specifies the required information and analytic dimensions. The overall objective of the model is to enable users to quickly understand and analyze the enterprise in “n” dimensions. For example, to view how the enterprise cost structure is impacted by the organization, suppliers, facilities and information technology and how the revenue structure is impacted by organization, product, channel, location and customer segment.
Rapid implementation for a digital model that ensures ROI on data infrastructure investments
The integrated analytics model described above may be assembled rapidly by using the patented CHORAL methodology. This is accomplished with an agile implementation method, delivering working solutions in rapid sprints and utilizing solution blueprints including configuration files and sample data sets. We have figured this out so you don’t have to.
Data may be obtained from multiple sources and integrated into a nodal network to create a broad, deep and multi-dimensional view of the enterprise.
Leading analytics from the C-Suite
The result is one application as a digital operating model to lead the enterprise. Complexity is reduced, the business value is realized, and an information advantage is achieved. Interested in learning more? Reach out to CHORAL to set up a demo and discuss your requirements.