The data you collect from customers tells you who they are, what they like, and most importantly, how to most effectively market to them. But in order to actually make use of this data, you have to know your Key Performance Indicators, how your data is organized, and which elements really matter for decision making.
To help you get there, 11 technology executives from Forbes Technology Council offer their best tips for companies who want to do more with the data they collect.
1. Know Your Key Performance Indicators (KPIs)
Collecting data is the first step, but you need to identify your KPIs. What in your data indicates you are doing well or doing horrible? Knowing how to whittle your data down to actionable metrics is where the magic starts. Based on your historical and current data, you can always know the health of your company. And you can make faster corrections to keep things on track. - Ryan Nelson, Grow
2. Make Organizing Your Data A Priority
The first step is to categorize your data into broad buckets such as “Support Data,” “Usage Data,” “Backup Data” and “Abuse Data.” From there you can figure out which data links most closely to the area of improvement you’re seeking and format it into digestible bits of information for the stakeholders involved. - Pin Chen, ONTRAPORT
3. Know What Actions You Can Take
Can you take actions on your data analysis? Reverse engineer metrics from desired final output/results. Then invite different departments and certified professionals (Legal, CPA, Ui/UX Design, Sales) to define metrics that will help them in their specific departments. - Arash Asli, Yocale
4. Introduce Structure And Automation Wherever Possible
I have two suggestions. First, make sure there is structure around how you process data, meaning an organized process for analyzing and executing on it. Second, most important is finding an automated way to put data to work. Companies have so much of it, and it multiples with time. It’s a machine’s job to keep up with and interpret it, but many companies sit around praying that their data scientist will somehow make it work. - Or Shani, Adgorithms
5. Set Storage Up In The Cloud And Store It All
Data is highly valuable and can be leveraged to improve business functions (i.e. service delivery) and maximize investments. Therefore, if your business has the ability to generate a lot of data, I consider it imperative that you set up storage in the cloud -- this will save you money -- and store it all. By "store it all" I mean that you should capture as much data as possible for later mining. - Scott Stiner, UM Technologies, LLC
6. Respect Data Privacy
Respecting data privacy needs to be the starting point for any data project. Unethical use of personal data can quickly erode trust, undermine customer relationships, and expose the company to brand damage. Be open about what you’re doing with consumer data, respect data provenance, and maintain data integrity. Companies need to adopt an active policy that defines data retention limits and periods. - Tim Barker, DataSift
7. Don't Be Afraid Of Big Data
Big data can glean meaningful results but it needs to be broken down for delivery. As a UX agency, we believe in simplicity: taking large amounts of data and boiling down the results to focus on what matters. By translating big data into visuals, such as graphs or infographics, you can reveal powerful insights that would otherwise be overwhelming or hard to deduce. - Andrew Kucheriavy, Intechnic
8. Decide What Elements Matter
You need to understand which signals in the data are important early or later in the sales cycle. If you use software that understands the KPIs and the data that matters that drive those KPIs, then you can uncover actionable insights in a timely manner. Analytics solutions that build contextual information around data convey instant insight that you can use to make strategic decisions and impact change. - Donal Daly, Altify
9. Build A Data-Driven Culture
It is important to get the whole company interested in data, not just the analysts. There are some things that we advocate, such as helping the whole company understand basic statistics and logic to form their own conclusions. We've also set up a "data wall" to showcase our latest findings. As well, we guide people to frame data-backed hypothesis so that we spend more time on experiments that yield results. - Ai Ching Goh, Piktochart
10. Avoid The Assumption Trap
Don’t fall into the trap of making assumptions and looking for evidence in the data. It’s too easy to find justification for your biases. Take a true data science approach: Describe your hypothesis, subject it to rigorous testing, adjust, retest and keep repeating this process. Allow properly tested hypotheses, not your mind, to guide your path to the most meaningful insights within your data. - Matthew Russell, Digital Reasoning Systems Inc
11. Don't Collect — Integrate
My best tip is to not think about collecting data, but to think about integrating data analytics into workflows. This way, companies -- via their monitoring tools and automation -- can quickly respond and impact business in real time. - Adam Wray, Basho Technologies
