“Data is Oxygen!” is a catchphrase popularized by IBM. At first, it may sound like an exaggeration. However, when we consider it in the context of useful information, data is gradually becoming a necessity for business survival, just like oxygen, whether you are a B2B or B2C company.
Several businesses recognize the importance of data integration into their processes and are taking full advantage. That’s why 65% of global enterprises increased their analytics spending in 2020.
Additionally, it’s not enough to rely on gut feelings when making enterprise decisions. In today’s business landscape, you can make educated decisions after examining historical facts and information on the subject.
Table of Contents
What is Data-Driven Decision-Making?
Being data-driven means relying on facts to establish patterns and insights into a subject. It removes subjectivity from your thought process before making important organizational decisions.
Making data-driven decisions involves collecting, analyzing, and studying specific information, then making it the basis for your actions. Essentially, a data-centric decision will have you look at and take insights from details regarding particular areas of your business.
Importance of Implementing Data-driven Decision-making for Startups
Data-driven enterprises experience over 30% growth yearly. If that isn’t convincing enough, consider the following additional reasons.
Helps in Making Faster and More Appropriate Decisions
Data is provable information gathered from real-life scenarios and events. So, they’re often quantifiable, reasonable, and justifiable. Hence, they provide a conducive premise for easy decision-making.
For example, say you want to select the best cloud-based collaboration tools for your team. You can make an effective decision by looking at reviews or user statistics and selecting the best one based on this data.
Prevents Making Biased Decisions
Prioritizing data-driven decision-making pushes you to ditch sentimentality for facts.
Essentially, looking at established data will help you identify and mitigate personal bias. It reduces your susceptibility to “gut feelings” decision-making.
Essentially, it enables you to be fair and objective in your judgment. The decision is no longer about what you think but what’s obvious based on the available report.
Deals with Unprocessed Data to Prevent Future Issues
Knowing you depend on data to make decisions will encourage you to invest more in ensuring it’s usable, consumption-ready, and accurate. Hence, to be data-driven, you must put significant effort into processing the available information into manageable and consumable formats.
This will give us data that can then provide insights into issues that may arise in the future, and you can quickly move to prevent them.
Aids in Setting Attainable Goals and Targets
Data clarifies what’s achievable and how to go for it. For instance, when you’re working with statistics, you’ll know what’s within or out of range. With that, you can set projections within reasonable limits using the data as your basis.
Gives More Room for New Business Ideas and Opportunities
Data exposes some aspects of a business or market that companies can leverage. Hence, it helps users see opportunities and give new ideas for maximizing them.
How Data Can Improve Each Business Area
Today, modernizing data integration has allowed organizations to effectively process and capture all types of data together. This means that different departments in a startup can work in tandem and benefit from data-driven decision-making, as we will see below.
Customer Service
- Enables personalized and tailored experiences
- Provides a better understanding of the customer journey and full purchase process
Finance
- Improves the accuracy of business predictions, forecasts, and goals
- Risk management and fraud detection
Operations
- Analyze process outcomes
- Automate and refine workflows
Management
- Predict industry trends
- Identify customer behaviors
Research and Development
- Detect new business opportunities for market advantage
- Enhance productivity by saving research time and cost
Sales & Marketing
- Improves lead generation
- Better pricing strategies
7 Steps to Enable Data-Driven Decision-Making at Your Startup
Now that you understand why you should prioritize data-centric decision-making, below are seven actionable steps for doing just that.
1. List Down Your Objectives and Priorities
It all starts with identifying the organizational goals and objectives. Once identified, it will help keep everyone aligned with the firm’s priorities over their team preferences. Remember, numbers and graphs are meaningless without setting the proper context in advance.
2. Collate Unresolved Issues
Determine the loose ends in your business. You want to tackle those first and put your house in order before anything else. Hence, you need to identify and collate the unresolved issues; these will be the first point of call in your data-driven decision-making.
3. Search & Identify Relevant Data Sources
With your goals and unresolved issues identified, you can start sourcing the needed data. And that begins with identifying the data sources.
How you go about this will depend on the type of data you’re looking at and your industry.
For example, supply chain-related data is very different from climate change-related data. Hence, you can’t treat them the same.
4. Organize and Analyze Data
Any raw data needs to be cleaned, organized, and analyzed before it can be used to garner insights. You’ll most likely gather unprocessed information if you’re sourcing the data yourself.
Of course, it’s understandable that you may not possess the requisite data analytics skills. You can use third-party software and AI solutions or employ data specialists to crunch your numbers.
5. Create a Data and Analytics Strategy
At this point, you’re already in the decision-making process, and data is driving it. Here, you should be able to look at the data-driven insights and create an actionable strategy with them. Based on your strategy, you can identify your key data projects.
You could even use a digital twin to provide you with these insights. It could be used, for example, to update KPIs in real-time as the KPI calculation could be linked to digital twins of your supply chain or asset management systems. This ensures that all of your team members have access to the necessary information that impacts their day-to-day decision-making.
6. Frequent Feedback and Going Back to the Drawing Board
After implementing a data project, you must resort to continuous feedback to gauge its impact on ROI. This further improves your project outcomes.
This feedback loop will enable you to radiate the success of your data projects across the organization, motivating your team and setting the path for future data and analytics adoption.
7. Encouraging a Culture of Data-driven Decision-making
The last part is creating a culture of data-centric decision-making in your organization. It shouldn’t stop with you or other leaders; ensure your employees don’t do things because they feel like it.
Encourage them to utilize data and credible information before deciding what to do.
An Example of How Data Enabled a Logistics Leader With Advanced Decision-making
Cold-chain logistics leader United States Cold Storage (USCS) partnered with Gramener to enhance its digital prowess across the value chain. Gramener helped USCS achieve its data analytics objectives with an ‘advisory-to-implementation’ approach.
Gramener worked with USCS teams to create the right processes, onboard skilled resources, and produce successful pilots. It started with creating a roadmap of the organization’s high-impact initiatives and taking chosen projects through a series of pilots to see which ones achieved good business outcomes.
With regular monitoring and measuring of ROI, the solutions were integrated into the business processes and adopted throughout the company by educating the users, sharing industry practices, and setting up a governance committee with standardized procedures in place. USCS was able to achieve a 16% reduction in warehouse dwell times and increase appointments by 13%.
Challenges For Startups Before they Set Up a Data-driven Decision-Making Framework
You must find solutions to the following possible challenges to any data-driven decision-making framework:
Data Quality
An overabundance of information means there’s too much noise, and quality data often gets buried underneath it. Hence, it becomes a challenge to excavate valuable information from the pile.
Over-dependence on Past Experience
Data are often observations from past occurrences. Of course, you can use them to make future projections through data visualization and analysis.
However, the basis for such forecasts is still historical information, which can be unreliable given that the world is constantly evolving.
Reliance on Gut Instincts
Occasionally going with gut instincts is okay. However, some managers often trust their gut too much. Your instinct will sometimes be wrong. But accurate data will always stay the same.
Also, you should avoid the error of forcing your researchers to produce reports that confirm your gut instinct. That’ll be a needless waste of time and resources.
Unconscious Cognitive Biases
Cognitive bias occurs when you rush decision-making without sufficient information. This may be the result of time-constraint or the decision-makers’ incompetence. Nevertheless, it remains a common problem in making data-centric decisions.
Data Helps Your Startup Make Better Decisions
Gut-feeling decisions are largely unreliable because there’s no basis for them. However, data-driven decisions are not only reliable but also justifiable.
So, the next time you make an important decision, base it on data. It could be as simple as deciding whether you should create an electronic signature or pursue an alternative.
Data will put you on the right path.
Note: This is a guest post from Jessica Day, Sr. Director of Marketing Strategy & Operations at Dialpad. Jessica is an expert in collaborating with multifunctional teams using cloud PBX systems to execute and optimize marketing efforts for both company and client campaigns.
