Using DataOps for Better Business
Discover how data operations can be optimized for stronger business decisions.
What Is DataOps?
Simply put, DataOps stands for “data operations.” Though the name makes it sound like a process run by IT professionals or a department that stays in its own lane, it encompasses every level and vertical within an enterprise.
It combines the processes of data analytics and the work of data engineers with a variety of data sources to make results clear for business users—the “data consumers.”
Definition of DataOps
When setting out to find how to start with DataOps, an enterprise must first define what DataOps means.
It combines all the best practices of people, the constant refinement and redefinition of processes to gather and use data, and the improvement of the technology that enterprises use to gather their data.
DataOps takes a process-oriented perspective on data. It takes advantage of the automation revolution to improve the quality of data gathered, the speed at which both machine-learning algorithms and human users can analyze it and put it to use, and how well those human users collaborate with one another.
The more eyes that can be put on a set of business data, the more conclusions the enterprise can draw from it, and the higher the quality of their business decisions.
Enterprise Data Operations: Why it Matters
Enterprises gather a huge amount of data. An organized, rigorous set of business processes is vital if large companies hope to sort through data from thousands or even hundreds of thousands of consumers.
Without a set of defined operations, information sits idle in a data lake, where it takes up valuable and expensive space without adding any real value to company operations.
A set of clear enterprise data operations standards make it easy to govern a company’s gathered information. Instead of a loose, disorganized pile of information, data operations help manage gathered information and take advantage of it in a useful, meaningful way.
Data operations can be sorted into three main categories: data governance, data mapping, and data monitoring.
Quality data governance ensures that the information an enterprise gathers can be accessed by the right people—and, more importantly—is inaccessible to the wrong people.
Certain customer data, such as demographic information, should be available to marketing departments that make judgments based on categories in CRM software but not accessible by accounts payable, who instead need those customers’ payment information.
Data governance also ensures that the data gathered is useful to the business and that the enterprise doesn’t gather data that doesn’t add value to company operations. As big data developments continue to evolve and empower enterprises to draw more insights from their consumer behavior data, it can be tempting to keep everything.
But storage is expensive, and high storage costs without a return on investment act as a money sink. Data governance ensures that an enterprise only stores information that adds value to its business.
It also ensures that both internal and external data is kept secure and gathered with integrity. Enterprises that gather data present a tempting target for cybercriminals in search of personal information to steal and sell, so these companies must put up a secure front to prevent data breaches.
Data governance, therefore, also covers cybersecurity and establishes standards to protect the data the company gathers—which protects the company itself from regulatory fines and loss of consumer trust.
Without data governance, an enterprise opens itself to enormous risks inside and out. Inefficient use of data slows operations and limits the value of the data that reaches business users.
When combined with the risk of loss without secure data storage practices, data governance acts as a foundation for the rest of the enterprise’s data use.
Since an ounce of prevention is worth a pound of cure, careful planning and quality guidelines during the governance stage provide a clear road forward without fear of problems down the line.
To use the data it gathers, a company must be able to locate the information it collects and collate it into a meaningful collection of information that can be used to draw insights.
Data operations cover this process through data mapping. Through both manual and automated processes, it matches information across databases to create a more detailed view of the business. It helps connect the dots to see which data has actual, valuable meaning and presents that data to decision makers.
Data monitoring, despite its name, is a far more intensive process than security oversight. It includes regular automated quality checks to ensure that data the enterprise gathers measures up to its internal standards.
It reviews every piece of data gathered and every database to confirm the data is:
Without the data monitoring arm of DataOps, an enterprise has no way to know if the data it gathers and organizes meets the requirements for its business needs laid out in the data governance planning.
Monitoring is a proactive, rather than reactive, process. Waiting until it’s discovered that the gathered information is tainted or incomplete in some way leads to a significant rollback. Enterprises that make long-term decisions based on flawed or incorrect data may struggle to pivot to a new set of procedures.
Instead, enterprises that regularly check their data for potential problems and take action to correct them before that data is used to drive decisions can use the data they gather with greater confidence.
DataOps ensures that businesses can take advantage of their data in the most effective way possible, acting with confidence and pulling information quickly.
DataOps and Cloud Management: Where Enterprises Are Failing
Multinational enterprises have access to more data than anyone else, including large governments. But many enterprises only take advantage of a small part of the massive cloud of useful information they have access to.
Adjustments to data processes and procedures help organizations take better advantage of data already gathered and make wiser decisions about new data to gather.
Not Modernizing Your Applications and Storage Options
The massive server rooms that marked enterprise data storage in years gone by are no more. When wondering how to implement DataOps, virtualized storage is the first step. Cloud computing is a necessity for modern enterprise data-gathering. It gives enterprises greater flexibility in the ways they access and use the data they gather.
Since enterprises gather information on a massive scale from a variety of machines and databases around the world, a physical location for their servers is too slow for the demands of modern business.
The virtual environment of cloud computing instead gives enterprises the ability to upload and download any data they gather from anywhere in the world and easily filter that data through automated virtual tools without excessive upload times.
When an enterprise first asks how to start with DataOps, it should begin with the conversion of all computing and data-gathering operations from a hardware mainframe or local server farm to a cloud-based model.
DataOps and Data Management: Best Practices
After an enterprise determines how to implement DataOps, it should adopt several best practices to better take advantage of the information it gathers.
Identify Business Goals
IT leaders aren’t the only users who benefit from gathered business data. Users across the entire enterprise must understand how best to leverage the data they gather to uncover new business opportunities.
As with any other business plan, an enterprise needs to know its future goals and how it plans to attain them. With those goals identified, businesses can use the insights they draw from their data to decide how best to move towards them.
For example, a business might seek to reduce cart abandonment rates in its online stores by 10% by the end of the quarter. By gathering information about user behavior, such as identifying bottlenecks in the user experience and the points where website users most often leave, it can improve the site design by the time the quarter ends.
Without identifying a goal, information about the user experience throughout the website provides no insights. Since data alone provides no valuable insights, enterprise users must approach it through a goal-driven lens.
For business users and decision-makers to make the best use of gathered information, they must be able to access that information without going through their IT team. IT teams have other concerns and responsibilities and can’t spare the time to assist business users in data analysis.
Instead, business users must have access to an interface so they can regularly and directly review data and better draw inferences from it.
Adopt a Cloud Architecture
A multicloud solution for mass data is an important step in DataOps implementation. Lyve Cloud provides an object storage solution for large amounts of information, ensuring that enterprises can securely store their information and access it with ease.
Enterprises can construct their own cloud architectures, but the development process is costly. Instead, a pre-built infrastructure designed for enterprises with a predictable pricing model makes it easy to slot cloud service benefits into an enterprise budget.
While data ultimately provides a valuable service for enterprises, it cannot be easily gathered, sorted, and filtered by human beings. Even basic data entry and sorting, if done manually, would take far too long for it to remain relevant, have a sample size too small to provide value, or demand a prohibitive cost in wages.
To effectively use data, enterprises must instead develop automated processes to gather and organize information. Data mapping and monitoring, which regularly review massive quantities of data beyond the capacity of any single person or team, are two of the processes which must be automated to provide any real benefits.
Data operations, therefore, includes not only the formation of data governance protocols and the designs needed for mapping and monitoring but also adhering to best practices throughout data operations and automating the necessary processes to make effective use of that data.