Businesses have been collecting data for decades, but we’re only just starting to understand how best to apply new technologies, like machine learning and AI, for analysis. Fortunately, the cloud offers tools to maximize data use. When starting any data project, the best place to begin is by exploring common data problems to gain valuable insights that will help create a strategy for accomplishing your overall business goal.
Why do businesses need data?
The number one reason enterprise organizations need data is for decision support. Business moves faster today than it ever has, and to keep up, leaders need more than a ‘gut feeling’ on which to base decisions. Data doesn’t make decisions for us, but rather augments and influences which path forward will yield the results we desire.
Another reason we all need data is to align strategic initiatives from the top down. When C-level leaders decide to pursue company wide change, managers need data-based goals and incentives that run parallel with the overall objectives. For change to be successful, there needs to be metrics in place to chart progress. Benchmarks, monthly or quarterly goals, department-specific stats, and so on are all used to facilitate achievement and identify intervention points.
We’ve never before had more data available to us than we do today. While making the now necessary decision to utilize your data for insights is the first step, finding data, cleaning it, understanding why you want it, and analyzing the value and application can be intensive. Ask yourself these five questions before diving into a data project to gain clarity and avoid productivity-killing data issues.
1. Is your data relevant?
- What kind of value are you getting from your data?
- How will you apply the data to influence your decision?
2. Can you see your data?
- Are you aware of all the data you have access to?
- What data do you need that you can’t see?
3. Can you trust your data?
- Do you feel confident making decisions based on the data you have?
- If you’re hesitant to use your data, why do you doubt its authenticity?
4. Do you know the recency of your data?
- When was the data collected? How does that influence relevancy?
- Are you getting the data you need, when you need it?
5. Where is your data siloed?
- What SaaS applications do different departments use? (For example: Workday for HR, HubSpot for marketing, Salesforce for Sales, MailChimp, Trello, Atlassian, and so on.)
- Do you know where all of your data is being collected and stored?
Cloud to the rescue! But only with accurate data
The cloud is the most conducive environment for data analysis because of its plethora of analysis tools available. More and more tools, like plug-and-play machine learning algorithms, are developed every day, and they are widely and easily available in the cloud.
But tools can’t do all the work for you. Tools cannot unearth the value of data. It’s up to you to know why you’re doing what you’re doing. What is the business objective you’re trying to get to? Why do you care about the data you’re seeking? What do you need to get out of it?
A clearly defined business objective is incredibly important to any cloud initiative involving data. Once that’s been identified, it’s important for that goal to serve as the guiding force behind the tools you use in the cloud. Because tools are really for developers and engineers, you want to pair them with someone engaging in the business value of the effort as well. Maybe it’s a business analyst or a project manager, but the team should include someone who is in touch with the business objective.
However, you can’t completely rely on cloud tools to solve data problems because you probably have dirty data, or data that isn’t correct or in the specified format. If your data isn’t accurate, all the tools in the world won’t help you accomplish your objectives. Dirty data interferes with analysis and creates a barrier to your data providing any value.
To cleanse your data, you need to validate the data coming in with quality checks. Typically, there are issues with dates and time stamps, spelling errors from form fields, and other human error in data entry. Formatting date-entry fields and using calendar pickers can help users uniformly complete date information. Drop down menus on form fields will reduce spelling errors and allow you to filter more easily. Small design changes like these can significantly help the cleanliness of your data and your ability to maximize the impact of cloud tools.
Are you ready for data-driven decision making? Access and act on trustworthy data with the Data and Analytics services provided by 2nd Watch to enable smart, fast, and effective decisions that support your business goals. Contact Us to learn more about how to maximize your data use.
-Robert Whelan, Data Engineering & Analytics Practice Manager
