In today's business landscape, companies across industries are intently focused on becoming data driven. Enormous investments are being made in data-related technology and data expertise to extract business value. But shockingly, most companies are failing. Where's the gap?
One Accenture survey found that only “32% of companies reported realizing tangible and measurable value from data,” and according to MIT Sloan, “In a recent survey of analytics leaders, 67% said organizational culture is the biggest barrier to becoming a data-oriented company.”
Based on these startling statistics, our team of data analysts created this guide with six strategic pillars that can help solve or mitigate some of the common shortcomings related to organizational strategy and culture.
One common mistake companies make when trying to shift to a data-centric approach is asking: "Which data do we have that can bring value to our business?" Instead, they should ask: "Which core business process can be improved if we have the right data?" Asking this question means focusing on your core business instead of focusing solely on data. Knowing what your company focus is enables you to identify which data you should be gathering and which data-based problems you can solve to bring maximum value to your company.
Again, most companies start data projects based on technology market hype instead of thinking about what makes the most sense for their business. Business strategy should drive technical decisions and not the other way around. So, when starting a new data project, we should always ask: how can our data bring us closer to achieving our business objectives?
Business and data are constantly changing, and so our data solutions should be constantly iterating too. If it takes months for an idea or improvement to make its way to production, it may already be outdated when it gets there. So having small iterations aligned with a fast operation that allows us to test our ideas quickly is critical for our company to have successful data models.
It is very common for companies to have data silos where a business area independently gathers data as part of their process or to support a particular analysis or report. The problem with data silos is that other areas may also be interested in the same data but are unaware of its existence, so they start gathering the data on their own, resulting in different versions of the same data, which may result in different values and different interpretations. Integrating those data sources into a data lake or data warehouse allows us to have a single source of truth, and it also allows each area to know which data exists that can improve their own analysis and decision making.
On the other hand, when companies start working with data lakes and data warehouses, the ownership of the data is then assigned to the data team. But the data team doesn't have enough business context to drive the decision making, which builds a wall between the data and the business. The best approach here is to keep the ownership of the data within a business domain and include the data professional into that scope, supporting the business to make data access, discovery, exploration and analysis easier.
Finally, using data as a business value driver requires a high-level culture change. It means asking the right questions, developing a solid process to answer those questions, and being open to exploration and experimentation. It means failing and adapting fast: learning from mistakes and adapting quickly is crucial for a company that wants to be data driven, since no matter how accurate your model is or how good your data is, it is ultimately only a proxy to reality and reality is always unpredictable.
Here at Avenue Code, we focus not only on the technical side of business transformation but also on the main agent of change inside the company and its biggest asset: people.
If you want to know more about how to maximize your data, be sure to check out the other blogs in our data analytics series: 4 Strategies to Boost Sales with Data Mining and Modernizing Your Data Warehouse with BigQuery.