The old way of looking at data – focused on reports and long-term projects – is over. The new decade of data in the 2020s is going to require a different approach. Doing more of the same, like training small numbers of professionals further on SQL techniques, will not be enough.
The Journey To Rich Data Experiences Starts Here
To leave behind the old world of business intelligence, it’s crucial to think about the big picture for a few minutes.
1. Find Your Data Why
Too much work in the data world is focused on incremental improvement. Some of these efforts – like training employees on data literacy 101 – certainly have value. For the most part, these foundational efforts are table stakes – most organizations are viable in these areas. Before you plan the next generation of data projects, it’s essential to think about your goals. Specifically, look at your goals for digital transformation. It is vital to think broadly at this point. To guide your thinking, use the following self-assessment questions:
- What competitors are we worried about from a digital transformation perspective?
- What parts of the business still rely on people carrying out relatively routine activities?
- What ideas are employees, especially new employees, excited about working on?
- If you could wave a “magic wand” of digital transformation and make a business process finish 50% faster, what would it be?
As you consider these questions, you will probably find that data is a central ingredient. For example, you might find your organization is lacking or deficient in machine learning or automated decisioning capability. In other situations a lack of quality data, or access to it, may hinder progress, or it could be the key to innovation your organization has a vision for but can’t execute without quality data to drive it.
2. Dream Beyond Business Intelligence
Now that you have a few broad company goals in mind, it’s time to think creatively about data. Traditionally, data has been used and consumed as business intelligence. This has mostly satisfied the answer to the question “what has happened?” In practice, business intelligence (BI) tools are limited to a relatively small group of people, and have not traditionally sought to answer the question “what will happen?” BI is great when you want a report to summarize activity. However, the capabilities of BI are no longer the limiting factor in using data.
To inspire broader thinking, consider these innovative data-driven examples.
- Enrich Decision Making With External Data.
During the COVID-19 pandemic, Chipotle Mexican Grill decided to take a preventive approach to the pandemic rather than sitting back. Specifically, the company leverages county-level health data and machine learning to inform decision-making. As a result, the company can now more effectively incorporate data in risk-mitigation decisions in its operations. This achievement is even more notable because the external data was not as predictable as internally produced company data.
- Growing The Market For Plus Size Women’s Clothing.
Dia & Co, a women’s clothing company, used data to help women find clothes that fit and look great. Before migrating to Snowflake technology, the company spent extensive time and energy on cleaning data. Today, the company utilizes data pipelines feeding its analytics and has seen significant improvemen
3. Map Your Chaotic Data Landscape
You’ve set your data goals and vision; now it is time to dig deep into your data chaos. Many companies struggle to innovate with data. These frustrations take different forms. Employees might have to submit approval requests to access data and wait for days to get started. In other cases, manual data clean-up to reach quality standards consume considerable time.
To map your data chaos, there are two broad areas to consider.
Technology Factors
Your current suite of data tools, including older data warehouses and analytics tools, could be holding you back. Start by creating an inventory of your data technologies and apps. If there are vital external data providers you rely on, include those in your list as well.
People and Process Factors
A company’s data culture can also add to the chaos. For example, IT security may not have the right processes to manage governance. As a result, they may feel like they have no choice but to resist data innovation because they fear data breaches. In addition, if data is perceived as complex and challenging to use, you may have a limited number of data experts (e.g., BI power users) who truly feel comfortable working with your data – or can even access it at all.
4. Enable Secure Data Access
Security and governance concerns are a significant challenge holding back data. To be clear – these concerns are entirely valid. No company wants to be hit by fines, lawsuits, or adverse publicity due to a data breach.
The good news is the historically common forced trade-off between security and accessibility is starting to fade away thanks to Snowflake. With Snowflake’s secure data access features, you can access data while ensuring that your data cannot be copied or transferred without permission.
5. Create An Impact With Your Data
The final step is to fuel specific, timely value with your data. The old way to create value with data could take days or weeks. A business team would discover or develop a use case. The team would then contact the data governance/ownership team through whatever medium and process was in place to access data or query. Then it’s back to calendars and coordination to reconvene the project team to review the data. Finally, if and when a course of action, direction or decision is made, the original use case may have changed, passed, or been deprioritized altogether.
Instead, look for ways to push data into active decisioning cycles. For example, you can inject data pipelines directly into customer-facing applications and let customers make more decisions. Shipping cost and potential delivery date data pushed into a consumer facing e-commerce application is an example. In addition, you can add more machine learning to make more small, routine decisions. You can start small and review the impact of machine learning. As you become more comfortable with the impact of data-powered machine learning, look at ways to create more impact with your data.
How To Accelerate Your Data Journey
Choosing the right technology – like Snowflake – will make a tremendous difference in your data journey. There’s just one problem. Learning a whole new way of thinking about data and a new data platform puts a lot of pressure on your teams.
That’s why Hakkoda has developed a Snowflake concierge service – an on-demand resource for training, onboarding, governance guidance, and training. We can also help you create no-code apps, automation and scale up your data initiatives. Contact us today to discuss your data transformation goals.
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