Tag Archives for " enterprise "

Strategies for Moving from Application to Enterprise Customer Master Data Management

Many organizations start their Master Data Management (MDM) journey in support of a specific sponsor business need.

Often hard-fought concessions are made to allow MDM techniques to support the data needs of a particular function.

Now, since almost every application needs your customer master, it’s time to expand the scope of MDM, replace substandard methods of accessing customer data in the enterprise, and establish the MDM hub for use by new applications across all customer business processes.

Crossing the chasm to the enterprise customer 360 can be daunting. During this 1-Hour Webinar, GigaOm analyst William McKnight will share some strategies for moving from application to enterprise customer MDM  and will discuss the ruggedization that must exist in MDM to be ready.

During this 1-Hour Webinar we will examine:

• How MDM is established with TCO
• The MDM Maturity Model (single application MDM is low)
• Elements in MDM necessary for takeoff:
  • A data quality program (prove data quality according to beloved peers)
  • DaaS SLAs – how do they work with the hub?
  • Operational integration (including data lake) AND value-added customer analytical elements (perhaps gleaned from transactional data)
• Syndicated data take-on
• Graph capabilities

 

About William McKnight

William McKnight has advised many of the world’s best-known organizations.

His strategies form the information management plan for leading companies in various industries.

He is a prolific author and a popular keynote speaker and trainer.

He has performed dozens of benchmarks on the leading database, streaming, and data integration products in the past year.

William is the lead data analyst for GigaOM and is the #1 global influencer in data warehousing and master data management and he leads McKnight Consulting Group, which has placed on the Inc. 5000 list in 2018 and 2017.

Source: gigaom.com

CIO Report: Enterprise Data Protection

Summary:

The enterprise data protection market still leverages a traditional approach to solving the increasingly complicated enterprise requirements.

Solutions range from on-premises traditional software to specialized appliances to cloud-based solutions.

Some solutions support a hybrid cloud to meet a specific enterprise’s requirements.

Most of the solutions take a traditional approach to backup and recovery while a few are looking to leverage newer data sources and technologies.

Key findings include:

• The data protection application is starting to disassociate from the underlying infrastructure.
• This opens new options for different delivery methods including the cloud.
• Cloud support exists to widely varying degrees. Solutions may support the cloud as a location to run the data protection application and/or as a source or target.
• Primary data sources are still the focus for most enterprise data protection solutions with limited support for non-traditional data sources including cloud, containers, and endpoints.
• Enterprise data protection solutions are starting to leverage machine learning and artificial intelligence to enhance automation capabilities. As enterprise requirements increase in complexity, so will the reliance on sophisticated automation.
• Strong management tools are essential for any enterprise data protection solution. The increasing degree of complexity makes automation and the overall management tool a core requirement.
• Support for regulatory and compliance requirements is in its infancy. Few products have yet to tackle this challenge. This could be one of the biggest opportunities for enterprise data protection moving forward.
• Simplicity is starting to make its way into enterprise data protection as solutions start to consolidate storage and data protection components into one system.
• Purchase models are moving away from traditional, perpetually-licensed software that runs on-premises to software that runs in a SaaS model from the cloud.

Source: gigaom.com

IoT in the Enterprise: Who’s Doing it, What’s Happening and What’s Working?

Whether in the mainstream media, the tech press or in business circles, a lot has been made of the Internet of Things (IoT). But IoT is about a lot more than internet connectivity for far-flung machines and devices. It’s about collecting sensor readings and other data from those “things.”

Why is providing value from that data so important? A confluence of factors in technology and business has given rise to IoT’s utility and the fascination around it.

 

Trends Giving Rise to IoT

The combination of cloud computing, streaming data, Big Data analytics, artificial intelligence, and the broader prospect of digital transformation have created a readiness in the market for generation, consumption, and analysis of time-series data that is machine- and sensor-produced.

While many of us may think of IoT in the consumer realm, conjuring up images of home automation and turning lights on and off with our personal digital assistants, it’s the industrial realm where IoT really shines.

For the most part, it’s sensor devices found in engine rooms, factories, elevators, automobiles (both self-driving and conventional), turbines, farms, and other industrial settings, that are leading the charge.

Sensors can report all types of conditions including temperature, moisture, pressure, the number of people in close proximity, the working order of a given component, or raw images and video.

Companies can monitor what these sensors are reporting, and can do so repeatedly, over very short intervals. While sensors used to just tell us if conditions were within a normal range, now they can do much more. Instead of just reading current conditions, this data can be saved and analyzed so that historical conditions can be correlated with certain phenomena.

Doing this facilitates the construction of predictive models so that certain sensor-reported conditions can be used to forecast specific phenomena. For example, patterns in the temperature of a piece of equipment may provide tell-tale signs of an impending failure.

And it probably goes without saying, being able to forecast such breakdowns before they happen can vastly reduce stress and revenue shortfalls stemming from lapses in operational continuity.

The above provides an operations-based explanation for why IoT is such a phenomenon, but what about the business reasons?

There are several. For one, companies are shifting their computing infrastructure from a mostly on-premises approach to a hybrid approach with some assets in the cloud. When analytics is one of the workloads that move to the cloud, things get teed up nicely for IoT.

Not only does cloud storage allow for arbitrarily large data sets, but it allows them to grow over time, even geometrically, as necessary.

And since sensor data originates from remote locations, storing it in the cloud works well. There’s no reason to store data on-premises that doesn’t originate there.

On the other hand, data that does originate from and is stored on-premises may be needed in the analysis work, along with the sensor data. That means the analytics system in play must be capable of both blending data from different data sets and working in a hybrid on-premises/cloud modality.

In fact, modern cloud-based analytics systems can do this very well, further facilitating the IoT workflows.

Perhaps the most important precondition for IoT, though, is the yearning businesses have to work with data in real-time, acting on new conditions as soon as they arise. IoT data—and the patterns for processing it—are completely aligned with that principle, often referred to as a data-driven culture.

Further, because IoT sensor readings are formatted as time-series data (successive point-in-time recordings of the sensor readings), they lend themselves well to AI and predictive analytics, both of which work extremely well with time-series data, as the predictions are often just additional, extrapolated data points in the series.

All of these technological developments and breakthroughs combine to serve as the cradle for digital transformation, a broad term that describes the journey to data mastery and data-driven outcomes.

In fact, when the essence of IoT is considered, digital transformation is what it’s all about: getting real-time data on the operations of a business, performing immediate descriptive and predictive analytics on it, and acting on the results.

 

Background on the Survey

With all of that in mind, Gigaom surveyed a large group of Enterprise customers (300 Enterprise IT Executives) to learn about the IoT initiatives in their organizations.

Our goals were to determine the inspiration for IoT initiatives, the business units responsible for pushing them, the groups that provided the funding, as well as those units that led them.

And beyond the quest for general knowledge about which business units play which role, we wanted to know how IT (information technology) and OT (operations technology) collaborated for IoT initiatives.

We also wanted to gain insight into the relative maturity of organizations’ IoT projects, and the organizations’ IoT maturity overall.

Also, as with all Gigaom surveys, we asked several categorizing questions—for example, size of the organization and the industry it works within—up-front, so that we could drill down to, and correlate answers to the other questions with these subgroupings.

Beyond learning who is driving IoT and what relative maturity of organizations doing IoT, we also set out to determine the scope of the projects, the general rate of success in their implementation, and how different companies implementing IoT approach vendor selection, as they proceed from planning to project implementation.

Like IoT itself, the overall goal of this survey was to discern things both descriptive and prescriptive. We wanted to see what customers have done, how much they’ve covered, how they’ve done it, and what impact these choices may have had on their success.

Read more: gigaom.com