The 5 Mistakes Ruining Your Data-Driven Strategy
And costing you money
Companies of all sizes have embraced using data to make decisions. However, according to a 2019 report from Goldman Sachs, it’s actually quite difficult for businesses to use data to build a sustainable competitive advantage .
Our team has worked with and for companies across industries. We’ve seen the good, the bad, and the ugly of data Strategy. We’ve seen teams implement successful data lifecycles, dashboards, machine learning models, and metrics. We’ve also had to come in and untangle, delete, migrate, and upgrade entire data systems.
Throughout these projects, we’ve seen several issues that pop up repeatedly: alack of data governance; bad data; complex Excel documents; a lack of alignment between data teams and the businesses; and an over abundance of dashboards, leading to confused decisions.
All of these data issues compound over time and slowly erode a team or company’s ability to trust and use their data.
In this article, we’ll discuss some of these issues as well as possible solutions your teams can implement to improve your overall data lifecycle.
Having Inconsistent Data and Sources of Truth
With all the various Business workflows, it’s inevitable that the same data gets entered in multiple places. One team might use Salesforce for one business process, while another might use Workday.
This, in turn, can lead to inconsistent data being entered at each step. Whether this is due to timing or human error isn’t the point. Once these inconsistencies start entering a company’s various data warehouses, they can wreak havoc on reporting.
Just ask any BI engineer or analyst who’s delivered a report to a director only to find it mismatched another report that was reporting a slightly different slice of data.
Regardless of the size or data maturity of a company, they all suffer from this. Our team has worked at multiple companies and consulted across industries, and they all face this issue.
Solution #1: Set up a data-governance strategy
This is usually solved by a data-governance strategy. data governance involves managing the data’s availability, usability, integrity, and security.
How you decide to deploy this data governance strategy depends on if you want to have a tight, centralized data process or decentralized, separate processes that occasionally meet up to assess that their core data models don’t overlap.
Data governance is far from a sexy term. It’s not data science or machine learning, and yet it’s the foundation of your data science and machine learning departments. Not having a solid handle on what your core source of truths are can lead to a lot of conflicting strategies.
Solution #2: Create a central data team to make decisions quickly
The other strategy is a little less recommended for large companies that want to move quickly, and that’s to develop a core data team. Their only focus will be to develop and manage data sets. This strategy works when your company is small because the data team itself will also be small.
This means when decisions need to be made on sources of truth and data integrity, it’ll happen quickly. There’s no need to manage multiple teams or to have a centralized meeting every month.
Managing Complex Business Decisions In Excel
Excel and spreadsheets continue to drive billion-dollar decisions in companies across the globe. This reliance on Excel has also leads to million- and billion-dollar mistakes by even the smartest companies.
For example, in 2008, Barclays agreed to purchase Lehman Brothers, except spreadsheet errors led them to eat losses on contracts they didn’t intend to buy . The detailed spreadsheet of Lehman assets contained approximately 1,000 rows that needed to be converted into a PDF. But the original Excel version had hidden rows with 179 items that Barclays didn’t want. The notes that they didn’t want those contracts weren’t transferred to the PDF but the hidden rows were.
