These days, most companies are taking a hard look at their asset portfolio in an attempt to figure out how they can wring the most value from what they have while still keeping their belts tightened that extra notch. Information technology assets are no different. Many in the C-suite these days are asking two things: How can we get better information to manage performance in such a complex environment? And how can we be sure that we’re making the right investments?
There are ways to get better information from IT tools. Many leading organizations are finding value in implementing business analytics solutions to drive more value from their IT investments and position their companies to survive and thrive in this brave new world.
Business analytics is not a tool or technology; it’s a concept. It refers to the use of certain technologies, skill sets and applications for the continuous exploration and investigation of business performance. Business analytics makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management to drive decision-making. There are several disciplines contained within business analytics: analytics, performance management, business intelligence and data management. Used together, the disciplines of BA are designed to help companies use data to draw hindsight, insight and foresight. The ability to provide foresight is what differentiates BA from its precursors.
The Analytics Information Management Maturity Model
Although many companies see a critical need to implement a business analytics solution, they are not sure whether or not they have the technical and cultural capabilities needed to execute an effective implementation. One common technique for assessing IT capabilities is to use an information management maturity model that facilitates the assessment, across a wide range of criteria, of an organization’s IT competence. In this article, we’ll use a custom IM maturity model that serves as a rubric by which companies can evaluate where they are on the continuum of the IT capabilities needed to support a business analytics solution.
Diving into the Information Maturity Model
There are typically three uses for information in any organization. Information can be used for tactical purposes, for insight into business performance, and to develop strategy and perform predictive and scenario analysis – i.e., for “how, what and what if” purposes. For each of these information uses, it’s essential to identify who uses the information and who in the organization is ultimately responsible for the quality of the information.
When describing an organization’s ability to use information for each of these purposes, it’s common to use the term “stages.” For our model, there are three stages: maturing, advanced and mastery. Few companies ever totally reach the mastery stage in all criteria, but many come close, and it’s a goal to always strive for.
Information Management Maturity Criteria
In order to assess the maturity of an organization’s IM capabilities, it’s necessary to review their achievements in the architectural and organizational culture, tools and technologies capability areas and determine which of the maturing, advanced and mastery stages they primarily fall within.
The beginning of the maturity continuum starts with the maturing stage. Here there is recognition of the need to have an increased collaboration with the business in order to develop solutions that meet tactical needs. Also in this stage is the start of defining business analytics and building more mature corporate controls and governance as the foundation for driving growth and advancing to the next stage of the continuum. In this stage, analytics efforts have more of a departmental focus and are led by information managers.
When we attempt to assess an organization’s IM capability vis-à-vis analytics, the first component we measure is the ability of the technical infrastructure to support analytics. The IT infrastructure consists of a group of shared, tangible IT processes, applications and hardware that provide a foundation to support information delivery as needed throughout the enterprise.
For our purposes, we’re going to assume that there is some level of analytics in place – or the desire to implement business analytics in the near future. Certain architectural capabilities underpin analytics across all stages of the maturity model. The first of these is the incorporation of built-in systems and information integration capabilities from source to target, to enrichment and aggregation.
The second is the ability to incorporate the use of multiple, different media types for information analysis – not just structured data. This is a highly critical capability to support business analytics, because the volume of unstructured data – in the form of social media content, emails, videos, etc., - is exploding, and this information is required to gain a full picture of what’s happening in the business environment.
An organization whose architectural capabilities are in the maturing stage should have information managers that understand the need to involve the business in the development of analytics capabilities. In this stage, the business may not lead the process, but they should be involved. The technical team should also understand the importance of how the analytics initiative can support the corporate strategy. However, at this stage, the analytics capabilities and strategic goals may not be effectively aligned.
Also during the maturing stage, the organization should have realized the need to develop a culture of corporate controls and compliance. Companies in this stage will most likely have a good start on a data governance organization and begin defining policies and procedures to support ongoing data quality efforts and compliance with both internal and external regulatory mandates.
Culturally, a fluid continuum of information practices is necessary to support an effective business analytics solution. Companies in the maturing stage of analytics competency focus on tactical execution. They likely have an enterprise data management strategy, organization and plan that is on equal footing (or perhaps a little higher) than the actual systems-development strategy employed by the company. As they move toward the advanced stage, their data management organization will typically be managed by a chief data officer.
Tools and Technologies
No analytics solution can be implemented without a broad set of tools and technologies with enabling standards to use them effectively. Metadata and data standards underpin analytics capability. Companies across the spectrum of IM capabilities understand the pivotal role metadata plays in the analysis and interpretation of data. Their technical infrastructure is typically designed so that it supports the following metadata types: intellectual property metadata, security metadata, authorization metadata, identification metadata, authentication metadata, data usage agreements metadata, data quality metadata, geographic information metadata and privacy agreements metadata.
Companies whose IM capabilities are in the advanced stage will have moved beyond maturing capabilities to a true understanding of the need to involve the business in all phases of implementing and sustaining business analytics throughout the enterprise. Most likely, a chief data officer will lead the analytics efforts. The CDO typically reports to the CEO and is responsible for setting the data strategy for the enterprise. Organizations that have a CDO will likely have their analytics solution very well-aligned with their corporate strategy and be positioned to move toward integration and insight to promote enhanced information sharing from both the internal and external perspectives.
In the advanced stage, there is also a balanced top-down and bottom-up approach to the IT project implementation. This means the IT strategy is developed both from the starting point of using the corporate strategy to determine analytics needs and using the needs of knowledge workers to develop those needs. The goal is to meet in the middle and take into account the needs of both constituencies when determining what analytics capabilities are needed.
As they move into the advanced stage of IM maturity, companies will begin to integrate business processes with their data management processes. For example, some leading companies employ business management strategies to track the production and consumption of data by business processes. This is so that proper metadata can be “wrapped around” the data to help decide if the data is correct, i.e., that it is based on knowledge of the business context that the data is to be used in.
These companies in the advanced stage of IM maturity will also have an efficient data governance organization in place. The established governance processes will span the data needs and will cover most types of interaction patterns being created, designed, implemented or retired. These include: internal/external, internal only, external only, or by type of business partner (e.g., government, competitor, customer, etc.).
Tools and Technologies
One development that marks a move into the advanced phase of IM maturity is the design of a metadata strategy and technical architecture to support it. This is foundational and is the fuel to drive data management, data sharing and future usage of data. The design effort typically utilizes resources with both business and technical knowledge, so that the applicable metadata is attached to each piece of data. The architecture is also flexible enough that, as new metadata requirements emerge, it can be tweaked to accommodate them.
The foundational piece of the metadata strategy and architecture is a metadata data warehouse and accompanying analytics capability. Metadata should be tracked almost like companies track stock tickers. Questions to ask, and answer, on a frequent basis include: What are the trends? Which data is most popular – i.e., which has the highest and/or lowest usage? What is the overall data consumption picture, and what are current “hot spots” of data consumption that may or may not continue – both within the organization and with external business partners? This analysis should include both data and process models.
As companies move into the advanced stage of IM maturity to support business analytics, they design their technical architectures so that it can support the most sophisticated analytics technologies such as simulation, scenario analysis and advanced predictive modeling. For example, the architecture typically supports the ability to analyze metadata statistics, as well as the timing of executed business workflows (both internal/external) to understand how data management activities are affecting business processes in terms of efficiency, capacity, performance and trends. It also supports what-if scenario modeling capabilities to analyze potential changes in the business mission, model, products and/or services mix. These capabilities are further developed as companies move toward the mastery stage.
The maturity continuum advances toward the mastery stage where the focus is from more of a strategic and predictive position. This is where information is managed, transparent and opened up to more of a mass audience. In this stage, organizations will have forward-looking capabilities with someone to fulfill the data futurist role, and analytics strategies will be in place to help drive the business.
As organizations move into the mastery stage of the IM capabilities needed to support analytics, they begin to practice both internal and external collaboration and information sharing. They begin to enhance information exchange with business partners to facilitate quicker, more cost-effective production schedules, inventory control processes – and to help them flexibly respond to changing business environments and customer demand.
As organizations move into the mastery stage of IM maturity, they often seek the counsel of a data futurist. A data futurist is a visionary in the future uses of data and of how to turn data into actionable information to support strategic and future information needs. These organizations also strive to create an open business environment and to proactively manage information transparency. They open up their information to the masses – albeit in a secure and trusted environment.
Companies that are moving into the mastery stage of IM maturity have a culture of fully integrated business process and data management that fosters extensive internal and external collaboration. These companies provide rewards for innovation through collaboration - not just internally, but also with external business partners and perhaps a few that are nontraditional partners (maybe vertically, as well as horizontally along the supply chain).
This is where the data futurist comes in. He/she can provide insights into what types of data will be needed, both for analysis and collaboration purposes, and can help develop methods and/or policies for collecting and disseminating the data. Toward this end, many leading companies have joined the movement away from developing custom software, toward implementing commercial and government off-the-shelf software and customizing it as infrequently as possible.
Tools and Technologies
As companies move fully into the mastery stage of the IM maturity needed to support business analytics, they extend their technology infrastructure strategy beyond the traditional data center boundaries. Their strategy typically incorporates the notion of cloud computing, local (or laptop, handheld, etc.) computing power, rather than relying solely on a company-managed and controlled data center. This is crucial for data sharing, transparency and external collaboration.
Business analytics is a powerful tool that can help organizations better understand what’s happening in their business environment. It can provide enhanced hindsight into what has happened, what is happening – and it can help management gain insight as to why events are occurring. However, perhaps the most powerful capability that business analytics provides, and what sets it apart from its precursors, is the ability it gives organizations to more methodically predict what might happen. They can ask questions such as, “What might happen?” “What’s the best that could happen?” or “How can we most effectively utilize processes, resource allocations and production schedules?”
To implement business analytics, though, it’s necessary to have an efficient, effective information management infrastructure in place. The IM maturity model we’ve presented here aims to help organizations assess where they are in their journey to implement a business analytics solution and get more value out of their business. However, the one major caveat we have is that it is not important where organizations start on the analytics journey – but that they start. Whatever their IM maturity level, any organization can begin an analytics initiative and build out IM maturity as they go. Are you ready to start the journey?
This publication contains general information only and is based on the experiences and research of Deloitte practitioners. Deloitte is not, by means of this publication, rendering business, financial, investment, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte, its affiliates, and related entities shall not be responsible for any loss sustained by any person who relies on this publication.
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/about for a detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries.
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