If you’re considering undertaking a data project of any kind, you’re likely to see a lot of data terms that sound very similar but mean incredibly different things. To get to the heart of what you are trying to accomplish, it is essential to define "data," understand each of the different data terms, and learn the interpretations to track and measure what it is you need to know to grow your business with accuracy.
Let’s look at some of the most widely used data terms that you may come across and examine how they apply to and can enhance your end goals.
The Structure of Data
The structure of your data should be your first consideration when setting up a data reporting plan or a database. Data input equals data output, which can affect your bottom line, so whatever is entered initially should be structured toward your objectives. Databases also require frequent management, cleansing, and maintenance to ensure your data is complete and accurate – which leads to informative and precise reporting.
The database is the heart of any data program. The term database refers to the collection of data points (you may be familiar with a CRM, which is one type of a database). This is a table, or a collection of tables, that contain the information you are tracking. A database can be full of contacts, deals, production schedules – anything. It can store very large numbers of records efficiently in a tiny amount of space (your data warehouse), so don’t be afraid to use it to its fullest potential for the best outcome.
Think of a data warehouse as being the data equivalent of any warehouse. It’s a collection of large amounts of data from various sources. A data warehouse allows you to store all the data from your company’s systems in one place and makes it easier to keep and access accessible historical records.
Let’s use a shipping warehouse as a physical example. In this warehouse, you have rows upon rows of racks stacked with various products. When an order is placed, the picker goes and retrieves the items from the racks and brings them to be combined into one shipment. This is the same way that a data warehouse functions, by storing millions of rows a data ready to be accessed and combined easily and efficiently.
Data governance refers to two different things, and what it is referring to at any given time depends entirely on the context.
#1: In terms of your business, data governance often refers to your specific collection of technologies, processes, and staffing necessary to maintain data quality, health, and stability, and access to your databases and data warehouses.
#2: On a grander scale, data governance refers to the governmental regulations regarding data security and retention, which vary based on locality.
Data Modernization and Data Migration
Modernization and migration of data are two terms that work in concert with one another. If you have been collecting and storing data for a long time, odds are your database system has been around since the beginning. Data modernization refers to the migration of data from an older legacy system to a newer system. This often requires re-configuring the structure and storage processes of your data to match the requirements of more modern systems.
Migration is the process of preparing and transferring data permanently from one system to another, be it a legacy system to a modern system or from one modern system to another. Data migration is a permanent process due to the unique structures of each data system and the requirements of preparing and transferring data between two disparate systems.
Working with and Managing Data
Are you tired of reading the word “data” yet? Hang in there as we explore a few more terms you need to know to work with and manage your data to take your business to the next level of growth, whether that’s by beefing-up internal processes or generating qualified leads and taking customers through the buying cycle to customer advocacy.
In a similar vein of making sure your database is set up for success from the start, it’s important to make sure you have a support structure in place to ensure the continued integrity of your data.
Data management comprises all the above terms – it’s the practice of ensuring the stability, architecture, accessibility, and accountability of all data owned by your company.
Data cleansing refers to the process of ensuring that data is complete, accurate, and uncorrupt. Data cleansing is an ongoing task that should be performed regularly, and gets progressively easier over time as new standards are developed and historical data retains its structure. Data cleansing is essential for any data management system as it makes sure all measures and metrics extracted from the data are accurate and complete.
Data Science and Data Scientists
Data science is the study behind data – encompassing the structure, management, and algorithms that define the data and how it is interpreted. Data scientists are people who have studied statistical measurement, calculation, and algorithmic analysis to help interpret a business’s data set to determine trends, predictions, anomalies, and KPIs.
Key Performance Indicators (KPIs)
Key Performance Indicators, often shortened to KPIs, are individual data points that help show how a company, department, individual, project, or process is performing. These are unique to every individual business and are based on the company’s specific requirements. To take a deep dive into Key Performance Indicators, check out this post.
Data visualization takes collections of data points and displays them visually on an automated data dashboard through graphs, charts, maps, or other means to aid in the interpretation of the data and trends presented.
Data dashboards are a great way to visualize common information in a way that allows data to be interpreted by a wide range of people within a company. Dashboards allow you to present managed and structured data for individuals, product lines, departments, or the full company.
Our last data terminology for this blog includes two phrases that you probably hear a lot about when it comes to the process of gathering data – data mining and big data. While these aren’t the only phrases in the data gathering process you may come across, they are two that you will see pop up often.
We hear a lot about data mining, often in negative terms, but data mining simply refers to the process of looking at large collections of data within a database to pull out new, relational pieces of data for your data sets. This is a wonderful tool for refining your business processes. When approaching data mining, it’s important to adhere to any regulatory or ethical considerations to avoid legal issues.Big Data
“Big Data” is a big-time axiom that really just means “all the data.” Big data is an extremely large set of related data points that can help identify trends and patterns. Big data is broad, it may cover a large number of demographics and use cases to allow for the analysis of data points from a wide array of breakdowns and viewpoints. Big data is essential in trend analysis and it helps to narrow in on true trends and identify any patterns that may be unseen in smaller data sets.
Atomic Revenue Helps Companies Thrive with Data-Driven Strategies
These are not all the terms you’ll see when exploring a data program, but it’s a great start. If you have questions about all-things-data, Atomic Revenue is a data-driven revenue operations company that helps clients from start-to-finish with everything you see here. Our team of experts and data scientists help define, set up, and manage data programs and processes to maximize end-to-end revenue operations tactics so your business can thrive. Contact us today for a no-obligation conversation and let’s resolve your data challenges together.