You Have Enough Good Data To Solve Problems: Here Is How To Get Started
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As a subscriber you can listen to articles at work, in the car, or while you work out. Subscribe NowThere is a myth that some businesses do not have enough data, or their data is too “dirty” to provide any real value. This false narrative is common among most industries, including manufacturing, home services, consumer goods, retail, and entertainment. Many believe that the power of data (that is widely recognized as truth in the tech and financial markets) is beyond their reach or not applicable to their industry.
Doubts about the promise of leveraging data for business growth often stem from uncertainty over how to get started or unsuccessful historical attempts at leveraging information to make decisions within a company. These struggles usually begin with having a poorly defined problem or solving the wrong problem. Ultimately, without a solid data strategy and plan for solution implementation, your data initiatives can result in small or even adverse business outcomes. However, failed attempts at leveraging data are not solved by ignoring the need to leverage data assets well.
There are four key strategies proven to enable data to work for a business (regardless of industry), which are outlined below.
Level Up Data Strategy and Engage the C-Level
The first challenge internal business insights or data teams have is ensuring they are working on problems that are a business priority. The groups are often found working on reporting and siloed ad-hoc needs and reporting into manufacturing, supply chain, or IT. The potential applications and opportunities to leverage the information a company owns are often not represented or advocated for in senior leadership meetings.
So while the business insights or data team might be celebrating wins, there is a risk these wins are small and non-sequitur to the more significant problems and opportunities that may lead to massive business impacts. To prevent this common scenario, businesses must have leadership advocacy and build and facilitate the cultural norm of data-based decision making.
- There must be buy-in, involvement, and understanding at the highest level for data science to truly make an impact on a business. Focus on the future and the role data will have in helping you get there faster and more efficiently.
Prioritize & Explore Underlying Problems
Deciding which data project to pursue first is a business and financial decision. Don’t get sidetracked by a shiny object that might provide unclear value at some point in the future. Large, complex projects that require significant coordination are often mausoleums where data projects go to die. Start with projects with clearly understood use cases that can be expressed in weeks and months. To prevent incorrect prioritization, scope the feasibility and impact of predictive analytics or data science projects before undertaking the project.
- It is vital to solving the most impactful problems first. Asking a lot of informed questions and determining the potential upside for any solution is a great way to begin this process, as well as having expert guidance to assist in scoping potential problems.
Focus On Business Integration
An experienced data science team knows how to help focus the business team’s efforts on two meaningful activities; integration and trust. In understanding the data science team will do the math, business leaders can focus on ensuring the solution will integrate into the business. This may take the form of initial pilot implementations, but ultimately address the application of the solution at a broader scale. The likelihood of measurable business impacts is increased by focusing on solution integration and how data will inform automation and persons.
- Understanding at the beginning of a project how you will use and implement the results of any predictive analytics, machine learning, or artificial intelligence algorithm is critical to success.
Build Trust & Leverage Outside Expertise
Leadership, management, and end-users are links in the chain of data science consumers. If one link does not trust the results (think recommendations), then the project is sunk from the onset. Building trust in your data science solution requires purposeful attention to fostering trust in the data science team’s results. Building trust is best accomplished as an integral part of the solution development process. Incorporating the business into the data science development process allows the business’s voice to be heard and have their subject matter expertise integrated into the solution.
Regardless of long-term staffing plans, leveraging an outside data science firm can help bring the experience and credibility into a legacy organization and, as a third party, also help with the organizational change and trust elements that are critical to success. Additionally, a data science firm rarely has preconceived notions or biases that can be difficult for internal managers and employees to avoid.
- Limiting biases and considering leveraging an outside data science firm can help ensure alignment and credibility of resulting recommendations from newly developed data science algorithms.
Ultimately, once it is agreed that there are experts to take care of the math and that you have enough clean data to work with, there are few excuses not to take action towards utilizing the data you have. Often, information is the most undervalued assets companies have, and in an ever-competitive marketplace, the ability to effectively leverage data assets is mission-critical — regardless of industry.