Data is becoming a must-have for many companies today. According to IDC, the Big Data and business analytics industry reached $189 billion in 2019, and could reach an estimated $274 billion by 2022.
But understanding how to use data effectively can be challenging, especially without a technical background. As the co-founder of Growth Marketing Pro, a blog that helps entrepreneurs and founders launch and grow their business. And you guessed it—data is a huge component of business growth.
So to help my community make better sense of their data and overcome common challenges, I spoke with Fabio Marastoni, a leading expert in the data analysis field. Fabio spent +10 years supporting Global Fortune 500 Companies in delivering omni-channel and digital transformation programs across different industries. Now he works with startups around the world to help them create data-driven processes that improve customer experience, drive sales, and increase revenue. Keep reading for some of Fabio’s tips for businesses looking to leverage data for growth in the new year.
Hailey: There’s a lot of talk about data these days, especially with terms like Big Data getting thrown around. Is there a difference between Big Data and other types of data?
Fabio: Good question. The answer is yes, Big Data is different from traditional data analysis, which is the kind of data most people have worked with—clicks, impressions, sales, etc. Big Data is much more complicated, and it requires an extremely high volume of information to process. That’s why you don’t often see Big Data initiatives from startups—a company that’s only a few years old simply hasn’t had the time to produce enough data to enter the realm of Big Data.
And once a company has enough data to sustain a Big Data initiative, it often causes headaches. Even large global enterprises who have Big Data projects struggle with it. You need to hire people with experience managing Big Data, there can be issues with data rights, and so on. For the purpose of this article, we’ll be talking about the traditional approach to data analysis only, which is most relevant to startups or small- to mid-sized businesses.
Hailey: Startup founders are focused on so many different things, from getting funding to launching new products. Why should startups invest in using data early on?
Fabio: Even with a limited amount of data, startups can see big benefits from using data. And you might be surprised to learn that smaller companies who start using data early on actually have a big advantage over larger companies. This is driven by two main factors:
- The organization is smaller
In a startup environment, processes usually aren’t set in stone, and new processes need to be designed often. Because of that, startups can easily adjust or create processes using their data, which can inform future systems.
- Culture is more fluid
Adherence to company culture is generally pretty high in startup environments. It’s safe to assume that employees joined the company because they believe in its mission and trust the founders. As a result, it’s easier to “convince” employees why data is essential to the company’s growth.
The startup mentality is all about trying fast and failing fast. Experiments are welcomed and encouraged because there’s no right or wrong way to do something (in the beginning). But when it comes to data, it’s important that startup employees track the data from those experiments so they can learn and use it to inform their next move.
Hailey: A lot of companies are adopting data analysis tools as part of their data initiatives. Can you provide a high-level overview of the data software landscape today?
Fabio: Using the right data analysis software can help startups make the most of their data. But there’s no denying the fact that companies are facing an explosion of Software as a Service (SaaS) tools right now. In fact, the average company spent $343,000 on SaaS tools in 2018, which was almost an 80% increase from the year before.
One of the reasons why SaaS has taken off is because it allows companies to optimize efficiency in specific areas of their business. It’s pretty common to see businesses employing dozens of SaaS tools to manage things like social media, hiring, CRM software, finance, email marketing software and so forth. But those platforms are all generating their own data, so now companies are having to learn how to manage that data and most importantly, put it to good use.
As a result, one of the key themes we’re seeing in the data software industry right now is the need for data centralization. The increase in SaaS tools has also increased the amount of data companies are dealing with—that can be both beneficial and harmful, depending on their ability to manage it. Having a single source of truth that houses their data helps companies make data-driven decisions and fuels more advanced processes that rely on artificial intelligence and machine learning.
The good news, is that there are data centralization platforms out there that are designed to help companies aggregate and organize their data into actionable insights. By eliminating manual data pulling, data centralization platforms save time and simplify processes like creating graphs and charts.
Hailey: What does it mean for an organization to be data-driven? How can companies increase their chances of winning in this new game?
Fabio: To be a data-driven organization means that the company is using data to make smarter business decisions, faster. Essentially, data is what informs every move they make. It helps companies evaluate their performance across multiple areas of the business so they can use those metrics to improve and revise processes. Using data also allows companies determine where they should be investing money, and where they should scale back. The list of benefits goes on and on.
But unfortunately, there isn’t a manual that holds the secrets to becoming a data-driven organization, and it doesn’t happen overnight. Becoming a data-driven company starts with people. It’s an all-hands-on-deck approach, and every employee needs to be on board in order for it to work. Companies should remember that the employees are the ones who will be physically tracking and implementing the data, so the human element is key.
Here are several things companies can do to move in the right direction:
- Weave data into the company culture
Instilling data into the company culture is one of the most effective ways to build a true data-driven organization. You can’t expect every employee to jump on the data bandwagon—developing a company culture that prioritizes data takes time. And executives should keep in mind that they can’t expect everyone to understand the complexity of data, nor should they try to convert every employee into a data scientist.
Company executives are in charge of shaping the culture—a top-down approach tends to work the best, so they should be demonstrating their commitment to data everyday. Communication is essential, and executives have to talk to employees about the approach, not just share the vision and values. Discussing the motive behind becoming a data-driven organization will increase participation.
- Design processes with employee experience in mind
It’s essential for data-driven organizations to work with clean data, otherwise, they won’t get accurate results. Having cleaned and structured data requires employees to put additional effort into the way they create and store data within all the different tools they use. That takes a lot of time and close attention to detail. So it’s important for employees to feel supported and have the tools to do their job effectively. If business processes aren’t designed with both the employee experience in mind and the data needs of the organization, employees could find shortcuts to workaround the extra effort, and end up with poor data.
As part of the initiative, companies should consider forming a dedicated data analytics department that reports directly to the CEO. Because employees will be using data more frequently, there should be a few elected people within the company who are leading the charge. They should be the points of contact for all questions related to proper data cleaning, storage, data entry, etc. Creating a data analytics team will help obtain the highest quality data possible across the organization, without slowing down the day-to-day operations of employees.
- Create a post-evaluation process
Any time a company designs a new business process or starts a new initiative, they need to have a clear process for analysis after the fact. It’s much harder to apply changes afterwards from an IT perspective, but also because it’s more difficult for people to change their habits. Creating a post-evaluation process must be applied cross-departmentally so executives can evaluate the efficiency between multiple business functions. To maximize efficiency, companies should have a dedicated function in charge of the post-evaluation process.
Before putting new systems into place, executives and the data analytics team should sit down with each department to jointly develop their post-evaluation processes. It should be specific, repeatable, and aligned with other processes being used by other departments. Consistency is key here, so companies should take their time developing the evaluation process, and maybe even conduct a trial run before deciding on which specific guidelines to use moving forward.
Final Thoughts
For small businesses, the topic of data can seem daunting. It’s hard to ignore, but it feels even harder to implement. Startups have so many competing priorities, and finding the time and resources to develop data-oriented processes can be difficult. But regardless of the company size, becoming a data-driven organization is entirely within reach.
It’s important to be patient, plan ahead, and make it a company-wide initiative. Remember that it doesn’t happen overnight, and be open to taking risks and making a few mistakes before you get it right. Making the investment in a data initiative will take your business to the next level.