Posted by David Kuketz on March 3, 2020
In so many asset-intensive organizations, the integrity of the maintenance data is imperative to the success of the financial, operational, compliance, and EH&S operations. In most organizations, data managers are aware of a possible level, accuracy, and completeness of the data set. To manage the issues, most create a continual and manual process to review and cleanse, repeating the process over and over.
The four (4) key data quality (DQ) dimensions are completeness, consistency, conformity, and consolidation (uniqueness). Data quality can be improved via cleansing (normalization, standardization), classification/coding, creation (enrichment, enhancement, construction), and consolidation (deduplication). Once you get data clean, you need to keep it clean. Perfect data equals perfect business (assuming people, process, and technology are not potential sources of failure).
Posted by David Kuketz on January 14, 2020
“I cannot figure out the value of my data!” Exactly! Art or science, theoretical. Few have connected the value of data to the value of a business or shown causality of quantitative improvement in Data Quality (DQ) to a measurable increase in business outcomes. If a race car team has a new motor with more output than the previous generation (or competitors), should they use the same fuel or improve the fuel quality to maximize the output of the new motor? The answer is, it depends on what the motor requires; that may determine whether they win races, or not.
Posted by David Kuketz on October 11, 2019
Today, companies are moving at the speed of thought, moving the needle on the KPIs for the outcomes that make them great companies. Great companies provide optimized customer experience management (CX) and operational excellence (OX), not to mention accurate, real-time reporting with full transparency of information to stakeholders.
Posted by Katie Mowery on March 24, 2017
Let me start off by saying that this blog has no direct tie to our business... but at Utopia Global, data is always on our minds and there’s no better time to “think data” than during the NCAA March Madness tournament.
Big Data is hitting enterprises at a time when we see the confluence of a number of other revolutionary changes, such as social networking, smart grid, smart sensors, GPS and smartphone location information. All manners of real-time data streams are adding to the volume, velocity, variety and complexity of the torrent of data coming into companies.
Contrary to perception, data does not sit static in a data repository. Data flows through an organization like blood in the circulatory system, and each day, each hour there are a myriad of touches to that “static” data. To the modern business, data is the crucial fluid that carries nutrients (information) to those business functions that consume it.
Businesses are using the power of insights provided by big data to instantaneously establish who did what, when and where. The biggest value created by these timely, meaningful insights from large data sets is often the effective enterprise decision-making that the insights enable.