How to Normalize Data: Put a Tiger in Your Tank
In the 1960s, the petroleum company Esso ran a highly successful advertising campaign urging motorists to “put a tiger in your tank.” It was a golden age for the automobile as tailfins shared the road with muscle cars, Americans crisscrossed the country on the new Eisenhower Interstate Highway system, and a gallon of gas cost about thirty cents.
Esso’s iconic tiger mascot was featured in a marketing blitz that boasted its gasoline boosted engine performance by starting faster, burning cleaner, and running more efficiently. Soon fake tiger tails were seen dangling from gas caps, and even blues legend Muddy Waters was inspired to sing, “I’ll put a Tiger in your Tank.” In the ‘70s Esso became Exxon and the OPEC oil embargo put an end to gas guzzlers, but the lessons of that time demonstrated the importance of fuel efficiency.
If it is true that data is the turbo-fuel of the digital economy, then it is also true that whatever data-dependent organizations can do to get more value out of their data becomes a business imperative. That includes data normalization, and that means it is important for today’s enterprise to understand what data normalization is and how to normalize data.
What is data normalization?
Data normalization is a process by which data is de-duplicated, grouped logically, formatted consistently, cleaned up, and stored in an organized structure. Once normalized, according to standard deviation, the data can be put to work for the organization through analysis and the application of resulting insights. Without normalization, an organization can collect and store all the data it wants, but most of it will either go unused, or it will be counterproductive, taking up space and producing imprecise (or even inaccurate) results and insights.
Now you can see why it is important to know how to normalize data. Data may be the new turbo-fuel, but it needs to be refined to produce better digital performance, operating efficiencies, and cost savings. And because each organization is different, and the ways in which they use and extract value from data are different, each organization will have different ways of approaching how to normalize data according to standard deviation.
Why is data normalization Important?
Because data is vital to the operation of businesses, as operations become more complex, the volume of data that is collected, aggregated, and analyzed is ever-increasing. If data is inefficient, the decisions and operations that rely on the data will be inefficient.
Gartner has found that, on average, as much as 40 percent of an enterprise’s data stores are incomplete or inaccurate. That means decision-making that relies on the analysis of enterprise data will be inaccurate, and Gartner says that can cost an average of $14 million per year. As operations and the enterprises become more complex, it stands to reason those costs will only grow.
That makes understanding how to normalize data a priority for every organization, especially considering the trends that are affecting IT operations. Adoption of 5G networking, deployment of edge devices and the Internet of Things (IoT), greater reliance on collaborative tools and the remote workforce paradigm all make data normalization vital as data collection and operations become more complex and central to the business.
Benefits of Data Normalization
As the foundation of more efficient IT operations through the adoption of AIOps, normalized data feeds a virtuous cycle that builds toward a stronger, more competitive organization. And because data normalization using standard deviation eliminates erroneous and duplicate data, it makes the analysis of the data that populates your data lake and other operational systems more efficient in several ways.
- Decisions and insights can be arrived at faster, leading to operational improvements such as faster mean-time-to-X
- The use of real-time, accurate data is the foundation for IT operations process automation which, over time, can become more beneficial.
- As more functions become automated, skilled IT staffers can be tasked with higher level responsibilities which can lead to better employee retention, increased profit/revenue, and higher levels of customer satisfaction.
- As your systems and services become more reliable, customer retention rates can rise.
As you can see, normalizing data in the context of IT operations, and current standard deviation data builds toward greater and greater business outcomes. Gains in service efficiency, availability, reliability, and quality pay dividends in ways that are measurable in terms of dollars and time, and also immeasurable when reflected in a stronger organizational culture.
How to Normalize Data for AIOps
While raw data does have its uses, clean, normalized data in an operational data lake is the fuel tank from which AIOps draws its power, and normalized data is the tiger in that tank. The normalization process begins with data collection. Every configuration item, every service, and every process that takes place produces data. It is therefore vital for the IT operations team to discover and monitor all those elements, and to do so in real-time. That’s because data has a shelf-life and a context in which it was created. Data must also be complete. A telephone number in which the last digit is missing has value to an analyst, but until the last number is identified, the information is incomplete and will produce a wrong result if used.
The collection of complete, accurate, and contextual raw data is the start. But the task remains: how to normalize data into the stuff of high performance? In the past, data normalization was a largely manual process. Organizations would collect data, put it in a data warehouse, and when it was needed individuals would go through the painstaking process of running queries and using various tools to find, clean, and format data for specific purposes. It was inefficient, error prone, and unreliable. In fact, as the era of Big Data arrived, many organizations found that the costs of data normalization outweighed the benefits, and so instead of learning how to normalize data, they abandoned the idea.
High-performing organizations, however, recognize that there is power in extracting the insights and potential of their data, and that by becoming better at how to normalize data, the benefits would be measured not just in the results of the task at hand, but in greater more efficient operations and optimal business outcomes from decisions made by extracting better insights. This is where AIOps sets organizations apart.
Automating Data Normalization with SL1
Whereas in the past data normalization was a mostly manual process, aided by tools that made the user marginally more efficient, today data normalization can be a fully automated process. In fact, the ScienceLogic SL1 platform is built on a foundation of machine learning that makes data normalization integral to the platform.
By operating at machine speed, and by eliminating the potential for human error, data normalization is a built-in feature of the data lake SL1 creates for each organization. As the SL1 platform discovers and monitors each configuration item, including those that are temporary or transitive, the application of machine learning means that raw data ingested into the organization’s operational data lake is quickly and automatically cleaned and normalized. That means it is checked for errors and redundancy and missing information, and the necessary corrections applied, before being rendered in a single, consistent format in the operational data lake.
But normalization goes further through the use of metadata that gives context to the data, including an understanding of how that data was affected by associated elements and also how it affects them as well. This dynamic process relies on accurate and consistent data, and because the ephemeral systems today’s enterprises rely on produce a large volume of data, they need to be connected to a repository that can collect, process, and reason over that data as fast as it is needed.
The ScienceLogic SL1 platform is not only able to keep pace with the volume, variety, and velocity of the kinds of data generated across the entire enterprise, but the machine learning algorithms it employs become more efficient at processing that data over time.
Put the Tiger in Your Tank
Now that you know why to normalize data, and how to normalize data, isn’t it time you started to normalize your data? Even if your past experience with data normalization was disappointing, you can have confidence that advances and innovations in IT operations management, and specifically with the ScienceLogic SL1 platform, have removed those traditional barriers and objections.
The Esso brand may be long gone, but if you are behind the wheel of a high-performance enterprise, you can still put the tiger of normalized data in your tank. Our team of experts has been working with forward-thinking organizations on IT transformations for years, helping them to overcome their biggest IT operations challenges. We’re certain we can help you as well. With a tiger in your tank, your enterprise will soon be purring like a kitten.
For more information about the data normalization and AIOps, view this eBook.