Buzzwords and jargon are unavoidable byproducts of advanced technology, and Data Analytics certainly contributes its fair share to the confusion. To demystify some of the more commonly heard and misunderstood terms, we decided to look at five of the most important buzzwords in the Data Management space today.
As Cassie Kozyrkov puts it, “Data is beautiful, but it’s decisions that are important. It’s through our decisions — our actions — that we affect the world around us.”
She should know, Kozyrkov, Head of Decision Intelligence at Google is an important player in this newer academic discipline that focuses on the process of selecting options.
At its highest level, Decision Intelligence is a mix of other disciplines. These include data science, social science, and managerial science for the AI era. Decision Intelligence empowers data engineers to lead AI projects responsibly. It also helps them design realistic and useful objectives, metrics, and safety mechanisms to automate data systems at scale.
On a more practical scale, Decision Intelligence uses data to help organizations optimize marketing and sales efforts and grow their customer base. It also allows organizational planners to automate and keep a tight reign over their inventory, pricing, sales, and margins.
Finally, Decision Intelligence can be a key contributor to maintaining an agile, efficient supply chain and keeping your customers happy where your competition struggles.
Data Accuracy is the holy grail of Data Analytics. It only stands to reason — if your data is inaccurate then every decision based upon it is potentially harmful to your business. It is easy enough to imagine the effects of inaccurate data in fields like business or finance, let alone in human terms when it comes to law enforcement or healthcare.
Gartner estimates that bad data costs companies 15 percent of their revenue annually. Their studies also indicate that this results in an average financial impact of 9.7 million dollars per year, per organization. The big picture is even worse, an IBM report indicates that in the USA alone, businesses lose over $1.3 trillion yearly to bad data.
But bad data can affect more than your bottom line. It can affect the overall performance of your enterprise and the ability of your employees to do their jobs effectively. A study by Forrester suggests that data analysts spend up to 40 percent of their time validating data before it is fit for use making strategic decisions. Bad data affects everyone in an organization from customer service reps to the purchasing department. Unfortunately, it is often at the expense of customer confidence and your company’s reputation. Tools like exMon ensure data accuracy by continuously monitoring your business data notifying you when something isn’t right.
Beyond all the hype, a Service Oriented Architecture is about interoperability. SOA’s make sure your apps and services play well together. They also help you scale as your business grows. SOA’s leverage individual services to offer business value. They focus on providing organization-wide strategic benefits rather than specific projects. Most importantly, SOAs don’t follow a one-and-done ethos but allow for rapid, continuous improvement without interrupting existing services.
SOAs structure software components as services available on demand. This makes them easy for developers to use and allows them to create consistent applications. SOAs also make new services easy to publish. They follow specific communications protocols that make them reusable wherever they are needed.
Finally, within an SOA, each service is easy to control and govern. By design, the individual components in an SOA are secure, both in the authentication procedures that give users access to the services and the actual communications between the components and the architecture itself.
exMon is a perfect example of a service-oriented tool that contributes to your strategic goals and continuous improvement without disrupting your business processes.
“Data must be a part of your culture, not just a program that people are granted access to. It must be a part of how people function, not just an icon on their desktop.”
-- From Data Culture -- The Only Way to Achieve Analytics at Scale
Analytics at Scale is a cultural approach to data that pervades every layer of an organization from the C-Suite to the front line. Success in scaling Data Analytics is based on these key components:
Achieving data at scale requires a financial commitment. A McKinsey study shows that companies that are willing to dedicate up to 25 percent of their investment budget are three times more likely to achieve success. This investment needs to be divided across the organization. Planned spending should include investing in data, technology, and talent.
Most importantly, companies need to invest in embedding analytics into their business processes and workflows. McKinsey reports that 90 percent of organizations that are successful in scaling their analytics dedicate more than half of their budgets to achieving this “last mile”.
This includes both trust in the data itself and the other stakeholders in the data collection, manipulation, and analysis process. Achieving trust boils down to providing proper training and involving everyone in the process from the top down. Leadership needs to empower people to make decisions based on the data available. Employees need to trust that the data is reliable and current enough to be used in real-time. Data management tools like exMon facilitate this trust.
Commitment or buy-in requires an organization-wide belief that data is one of your organization’s biggest assets. Leaders must demonstrate that their decisions are data-fuelled rather than based on gut feelings or instinct. They also need to monitor to make sure everyone down the chain is using data in a way that aligns with the organization’s strategic goals. This means making it clear that collecting and assessing the right data is as much a priority as identifying missing data and closing the gap.
“A data culture is made up of data people… If they don’t understand how to work with data, they can’t be data-driven.”
Data has to be a part of everyone’s job description. This goes beyond simply hiring people with data experience. It also involves transforming your workforce by training existing employees and redefining your processes and responsibilities. The goal is to create a data-focused environment. An environment where people have the confidence to identify the right data and communicate their findings to improve your business.
A data-focused mindset prioritizes data and creates a level playing field. This allows everyone in the organization to discover insights, innovate and contribute to your goals.
Read more about how exMon supports fostering this mindset by keeping stakeholders involved, accountable, and willing to share ideas. Read case stories here.
Self-service Business Intelligence is a key component to achieving data analytics at scale. Simply put, Self-Service BI gives business users that don’t have a background in data science the ability to access, filter, sort, visualize and analyze data on demand. Most importantly, they can do it without involving your data engineers or IT teams. By implementing self-service BI you gain the following benefits:
Free up your data specialists and IT staff for higher priority tasks by providing business users with automated, ad-hoc access to their own queries, dashboards, visualizations, and reports.
Speed up your data analysis and decision-making process by shifting analytics work to your business stakeholders rather than your limited BI team. Self-service BI helps you avoid bottlenecks and accelerates your business processes by giving decision-makers the information they need to take action.
It’s one thing to talk about data at scale or a data-driven culture, but self-service BI makes it a reality by providing data directly to your executives, managers, and workers.
Self-service BI makes your organization more agile. By expanding data availability throughout your organization, you also accelerate your decision-making process. For organizations that want a competitive edge, self-service tools instantly provide information where it is needed most.
As you can see, Data Analysis and Data Management involve a series of interlinked concepts and practices that, if done right, affect your entire organization.
For more information about how you can take control of your own data culture, check out the exMon platforms to find out how that will help you.