Standardized IT systems can appear to make a lot of sense – standardization can be cost efficient, aligned with industry approaches and can help promote re-use. However, the business advantages standardization yields can be easily replicated by competitors. So what are the trade-offs and when does it make sense to choose standardization?
Standardization is typically preferred when cost efficiency is the motivation and/or when there is a requirement for interoperable IT components and process interfaces. The cost motivation is straightforward and is particularly appealing to organizations that are subject to cost pressures in their own market. Selecting standardized technology can yield immediate savings. The combination of multiple suppliers and ease of interchangeability creates a buyer's market. This can stimulate ongoing price reductions. The drive to achieve cost efficiency through standardization can be seen in all stages of technology deployments. In the design stage, selecting standardized technologies can increase interoperability and reduce complexity in system design. In the deployment stage, standardized products can enable a more predictable operating environment that is typically less costly to manage. In the operational stage, use of more standardized technologies may increase access to skilled personnel. Interoperability is an increasing concern for many organizations, especially those that operate in markets where eco-systems and partnerships are critical factors or those markets in which merger and acquisition activity is common. The process of linking or bringing together multiple heterogeneous IT systems can be greatly simplified when IT environments are built from highly standardized systems.
Joining in on the spirit of all the 2013 predictions, it seems that we shouldn't leave data quality out of the mix. Data quality may not be as sexy as big data has been this past year. The technology is mature and reliable. The concept easy to understand. It is also one of the few areas in data management that has a recognized and adopted framework to measure success. (Read Malcolm Chisholm's blog on data quality dimensions) However, maturity shouldn't create complancency. Data quality still matters, a lot.
Yet, judgement day is here and data quality is at a cross roads. It's maturity in both technology and practice is steeped in an old way of thinking about and managing data. Data quality technology is firmly seated in the world of data warehousing and ETL. While still a significant portion of an enterprise data managment landscape, the adoption and use in business critical applications and processes of in-memory, Hadoop, data virtualization, streams, etc means that more and more data is bypassing the traditional platform.
The options to manage data quality are expanding, but not necessarily in a way that ensures that data can be trusted or complies with data policies. Where data quality tools have provided value is in the ability to have a workbench to centrally monitor, create and manage data quality processes and rules. They created sanity where ETL spaghetti created chaos and uncertainty. Today, this value proposition has diminished as data virtualization, Hadoop processes, and data appliances create and persist new data quality silos. To this, these data quality silos often do not have the monitoring and measurement to govern data. In the end, do we have data quality? Or, are we back where we started from?
'Reducing complexity' is a reason frequently given by organizations when instigating an application rationalization initiative. However, complexity can be challenging to define and measure in all but subjective terms. For many organizations embarking on rationalization, the primary focus is the reduction or elimination of complexity. Complexity is often associated with higher costs and reduced adaptability or agility. However, where the value of complexity to the organization is high (due to the competitive advantage or ability to maintain/defend a market position and brand equity), these may be prices worth paying. EAs play a critical role in defining those areas where complexity can add value and what the trade-offs are. This enables organizations to take a more rounded view of complexity in the context of application rationalization.
Complexity to Achieve Market Differentiation
When competitors use similar IT systems, their capabilities and efficiencies in serving customers probably will be similar to yours. In effect, this makes organizations more interchangeable. To mitigate this, organizations seek an edge in terms of service differentiation, capability or cost. This consideration is particularly important in highly populated marketplaces. For example, many services organizations use quality of service or customer reputation as key tools for online sales. This means that IT capabilities and efficiencies play a major part in defining these organizations' relative market positioning.
Security and privacy have always been at the core of data governance. Typically, company policies, processes, and procedures have been designed to comply with these regulations to avoid fines and in some cases jail time. Very internally focused. However, companies now operate in a more external and connected fashion then ever before.
Let's consider this. Two stories in the news have recently exposed an aspect of data governance that muddies the water on our definition of data ownership and responsibility. After the tragedy at Sandy Hook Elementary School, the Journal News combined gun owner data with a map and released it to the public causing speculation and outcry that it provided criminals information to get the guns and put owners at risk. A more recent posting of a similar nature, an MIT graduate student creates an interactive map that lets you find individuals across the US and Canada to help people feel a part of something bigger. My first reaction was to think this was a better stalker tool than social media.
Why is this game changing for data governance and why should you care? It begs us to ask, even if a regulation is not hanging over our head, what is the ethical use of data and what is the responsibility of businesses to use this data?
Technology is moving faster than policy and laws can be created to keep up with this change. The owners of data more often than not will sit outside your corporate walls. Data governance has to take into account not only the interests of the company, but also the interests of the data owners. Data stewards have to be the trusted custodians of the data. Companies have to consider policies that not only benefit the corporate welfare but also the interests of customer and partners or face reputational risk and potential loss of business.
In a month or so I’ll be launching a survey to research issues around information strategy, information architecture and information management in general. I thought it might be useful to do a bit of crowdsourcing to get the best ideas for what questions to ask and make sure I’m covering your top-of-mind issues. We ask you all fairly often to provide answers to survey questions – maybe you’d like to provide input into the questions this time out?
Surveys are interesting – one is tempted to ask about everything imaginable to get good research data. But long onerous surveys produce very low percentages of completes vs. starts -- it’s classic case of less is more. Twenty completes for a very comprehensive survey is nowhere near as valuable as a couple hundred completes of a more limited survey. For example, I really wanted to provide an exhaustive list of tasks related to information management or information architecture practices and then provide an equally exhaustive list of organizational roles to get data on who does what in the typical organization and what are the patterns regarding roles and grouping of responsibilities. But the resulting question would have been torture for a respondent to go through, so I edited it down to the 15-ish responsibilities and roles you’ll see below, and I’ll probably have to reduce the number of roles further to make the question viable.
So, below are the questions I’m thinking of asking. Please use the comment area to suggest questions. I can’t promise to use them all but I can promise to consider them all and publish some of the more interesting results in this blog when they come in.
A common question Forrester gets from organizations planning an application rationalization strategy is “How many applications should I aim for?” It is a good question, but can be symptomatic of an approach where less equals success. This is very common with executives and senior stakeholders, who often can take this type of singular view of application portfolios. While it is a straightforward way of examining your application portfolio, inherently it is a binary dimension. It does not account for the multiple prisms through which organizations should view and make decisions about their application portfolios.
Enterprise architects can help organizations determine where their objectives are placed on a wider range of applications continuums than just the number of applications, for example:
§ Customized Vs Standardized– What degree of customization do my organization's applications require? What differentiation does standardization provide? What is the opportunity cost of customization? What is the right balance for my industry sector ?
§ Control Vs Freedom – What level of freedom on application choice best serves my organization? How much control of the application portfolio is appropriate or mandated for my market and what is the cost of this? What is the cost/benefit of decentralization of choice compared with control of choice?
§ Centralized Vs Localized – What differentiation is provided by localized applications? What is the opportunity cost of centralized applications? What is the right balance of localized differentiation and centralized standardization for my organization?
In a recent blog post, I mentioned that hiring and developing the right people for EA’s strategic aspirations will be a bottleneck, and as a result, development and certification programs will become a popular topic of conversation. Well it didn’t take long for some client inquiries to come in and show me that while I may be on the right track with that prediction, there's more to the topic than one might think. From the looks of it, the challenge runs deep: Development is about more than skill sets and certifications - it’s also about real career path - and elevating the job to the level of "profession" akin to those that architect our buildings. To get some more information, I called Dr. Brian Cameron, Founding President of The Federation for Enterprise Architecture Professional Organizations (FEAPO) and Program Director of Penn State’s Master of Professional Studies in Enterprise Architecture. Here’s what he had to say:
Q:Our clients are interested in developing their teams as opposed to hiring, due to challenges with funding and attracting talent. What are members of the EA community doing about this? Is it more common than not?
One of the great things about working in enterprise architecture is the opportunity to work on a diverse range of initiatives. Findings from our Global EA Maturity Survey in Q2 2012 show it is going to be a busy 2013 for enterprise architects:
While variety is to be expected in the enterprise architecture role, EAs will be multi-tasking more than ever. As businesses increasingly experience the value of enterprise architecture, the demands on the EA function increases also. It is a good problem to have - but it is a problem nonetheless. Equally, many of the priorities are linked and progress (or lack of) in one area informs another. For example, strengthening emerging technology processes and simplification of the application and technology portfolio are interlinked. At the same time, many businesses expect results in shorter timeframes and some of the priorities are inherently longer-term and deliver over an extended period of time - which is not always fully appreciated by stakeholders. These are clearly challenges and although it will be a busy year, EAs can look ahead with confidence. Demand for EA services are growing. Businesses are looking for more from their EA functions, in more parts of the business - and the opportunity for developing the scope, importance and relevancy of EA is ever growing.
Data management is becoming critical as organizations seek to better understand and target their customers, drive out inefficiency, and satisfy government regulations. Despite this, the maturity of data management practices at companies in China is generally poor.
I had an enlightening conversation with my colleague, senior analyst Michele Goetz, who covers all aspects of data management. She told me that in North America and Europe, data management maturity varies widely from company to company; only about 5% have mature practices and a robust data management infrastructure. Most organizations are still struggling to be agile and lack measurement, even if they already have data management platforms in place. Very few of them align adequately with their specific business or information strategy and organizational structure.
If we look at data management maturity in China, I suspect the results are even worse: that fewer than 1% of the companies are mature in terms of integrated strategy, agile execution and continuous performance measurement. Specifically:
The practice of data management is still in the early stages. Data management is not only about simply deploying technology like data warehousing or related middleware, but also means putting in place the strategy and architectural practice, including contextual services and metadata pattern modeling, to align with business focus. The current focus of Chinese enterprises for data management is mostly around data warehousing, master data management, and basic support for both end-to-end business processes and composite applications for top management decision-making. It’s still far from leveraging the valuable data in business processes and business analytics.
The number one question I get from clients regarding their data strategy and data governance is, “How do I create a business case?”
This question is the kiss of death and here is why.
You created an IT strategy that has placed emphasis on helping to optimize IT data management efforts, lower total cost of ownership and reduce cost, and focused on technical requirements to develop the platform. There may be a nod toward helping the business by highlighting the improvement in data quality, consistency, and management of access and security in broad vague terms. The data strategy ended up looking more like an IT plan to execute data management.
This leaves the business asking, “So what? What is in it for me?”
Rethink your approach and think like the business:
· Change your data strategy to a business strategy. Recognize the strategy, objectives, and capabilities the business is looking for related to key initiatives. Your strategy should create a vision for how data will make these business needs a reality.
· Stop searching for the business case. The business case should already exist based on project requests at a line of business and executive level. Use the input to identify a strategy and solution that supports these requests.
· Avoid “shiny object syndrome”. As you keep up with emerging technology and trends, keep these new solutions and tools in context. There are more data integration, database, data governance, and storage options than ever before and one size does not fit all. Leverage your research to identify the right technology for business capabilities.