Big data analytics is becoming more effective with cloud computing. The cloud shifts the analytical computing activities to a centralized, shared server. Thus, the data and its processing are housed away from the devices used to access the data and display (visualize) the results. 

It’s not always clear where to begin on analytics projects. This Advisor provides some guidelines on how your business can get started.

In this Advisor, I offer one company’s experience of moving to a cloud HRIS system.

The components embedded in the Data Value Map (DVM) provide a foundational shared language that allows meaningful group conversations to take place; conversations that all parties can understand and to which they can contribute. The two examples illustrated in this Advisor demonstrate how the DVM can help build a shared understanding around organizational data strategies and data projects.

The knowledgebase is the foundation of business architecture. Without it, we cannot achieve the benefits and vision that the discipline intends to provide. A business architecture knowledgebase enables an organization to do the following:

With the rise of social media, big data, and the heightened emphasis on the customer experience (CX), it is more important than ever to build an organizational leadership team committed and laser-focused on implementing the necessary behaviors and orienting business operations to continuously improve CX.

According to our research, the biggest obstacle to enterprise artificial intelligence (AI) adoption is a lack of available experts skilled in AI development. So how are organizations meeting or planning to meet their AI implementation needs? Results from our ongoing survey examining the adoption and application of AI technology in the enterprise — based on the initial responses from 107 participating organizations worldwide — helps to somewhat clarify this question.

I don’t know how to best identify the point in time when computers merged with communications and became customer utilities and entertainment devices, but clearly that transition was a major step toward creating the digital environment we all live in today.

The task organizational leaders face right now is how to turn their companies into resilient organizations. Though this is a question far beyond the scope of an Advisor, there are three basic capabilities resilient systems need to show: speed, flexibility, and diversity.

For nearly all counter-strategies, a company must look at its digital assets in relation to each other and to the present digital landscape — that is, the enterprise architecture.

Here, we present the Data Value Map (DVM), a discursive template that facilitates the development of a shared understanding around data. The template helps enable open conver­sations and a co-construction of understanding between the parties involved, which can promote a value-driven approach to data projects.

Data-driven decision management is an approach to managing business that focuses on decision making that can be backed up with verifiable data.

We need to understand that a customer’s interactions with our company are just a small part of a bigger picture. The sum of the interactions a customer has with our company isn’t the customer experience, that is just part of the whole customer experience.

One thing must be understood clearly for an Agile approach to be applied to a data warehouse or any other BI development: user value is not found in the application, but rather in the information availability for reporting and analysis. Next to the availability of information, the data warehouse can provide additional value to the user by providing insight into the sourcing of data and the quality of the resulting information.

Whether we choose to face it or not, digital disruption is here to stay. Whether we choose to accept it or not, digital disruption will affect every enterprise. The jury on our digitized future is still out, and it may be some time before it comes to a unanimous conclusion. In the meantime, there are plenty of serious writers and researchers who predict some significant changes in our world. In this Advisor, I sketch some of these ideas so that you can consider them in your future planning scenarios.

Agile development has long been considered for building small stand-alone systems. However, there is considerable disagreement as to how well Agile works for large, complex enterprise-class data integration initiatives, such as an enterprise data warehouse or enterprise metadata management. An obvious question the data management community has to ask is whether Agile can be used for projects like business intelligence.

A key question I’ve had for some time is: how well are enterprise AI applications living up to expectations? Initial results from our ongoing survey covering the adoption and application of AI technology (based on initial responses from 105 participating organizations) provides some insight into this question.

If electric cars and trucks are the vehicles of the future, then that already suggests a massive change in the industrial infrastructure — new modes of manufacturing, new modes of refueling and repair, and, perhaps, new roads. Given the key role of autos in society, such a transition will be a major story. However, it is really only a small part of the changes facing the automobile industry.

The real “lean” in the Lean world (or the “agile” in the Agile world) will manifest only when an organization’s focus is on the development of the whole organizational system.

One way to better understand the customer is through data about that customer.

All organizations have a business architecture, but it may not be written down. And if it is, it may not represent a common enterprise perspective or be documented in an industry standard way. Thus, organizations that desire to leverage business architecture for transformation, better strategy execution, or various types of decision making need to build their business architec­ture knowledgebase first before they can fully benefit from the discipline.

The ever-increasing demand for cloud computing services deployed across multiple cloud data centers (CDCs) necessitates a significant amount of power, resulting in high carbon emissions and a negative effect on the environment. In sustainable cloud computing, renewable energy resources power the cloud data centers, replacing the conventional fossil fuel–based grid electricity or brown energy to effectively reduce carbon emissions.

Agile organizations need a description of how teams work from beginning to end. The Agile product development framework will illustrate how operational and delivery risks are evaluated and reduced in an innovative environment.

Designing cognitive computing systems (CCSs) requires a strong case for the investment into those systems. Organizations must not only be able to justify the initial investment into developing a CCS, but also think through the investments that will be needed to ensure it can be refined and enhanced over time.

Many enterprise architecture (EA) teams struggle with creating a program that demonstrates the level of strategic value that they believe EA should have. Taking actionable steps to remove many of the most common roadblocks to growing the strategic nature of your EA program takes time. For most programs beginning the journey to strategic engagement, I strongly recommend dividing up the enterprise into business segments and applying EA to one (or at most two) segment(s) at a time.