Gone are the days of hinging the success of companies on gut-feel decisions and outdated processes, or expecting long-standing business challenges to solve themselves- we now know to turn to analytical solutions and harness the potential of data for answers. Recent developments in the analytics landscape have even made the utilization of advanced analytics in these solutions possible. There’s nothing more exciting than setting our sights forward and using our data to not just describe the present, but predict and prescribe solutions for the future, but how can we know we have the right resources and reasons to get started? Here, we’ll walk through some of the critical factors that preempt data science institutionalization and discuss what makes a use case a good fit for data science.
You might be reeling with ideas for how to enhance your business processes with machine learning, but before your project takes flight, have you taken an inventory of what it’ll need to soar? Data science is about as complex as it is exciting, but the right combination of resources can help prepare your organization for any bumps in the road ahead.
Sowing the seeds of a successful data science initiative deeply involves the people in your organization. The development of an analytics-centered culture as a whole, where data has a seat at every table and decisions are largely based in fact, is an important accelerator for data science projects. An analytics culture provides the momentum projects need to be carried into production and ensures the time and effort put into building machine learning solutions is not done in vain.
If your employees are distrustful of data, even the most technically capable data science pipelines will lack in adoption.
Fortunately, many techniques exist to abate the data-resistant mindset that may pervade your company. An executive sponsor or two for your data science initiative can help champion the solutions built throughout the company and provide a greater sense of trust in your findings. Holding data science workshops or brief training sessions can also garner organizational support by piquing employees’ interest and pulling back the curtain on how advanced analytics techniques work.
Of course, data science also requires a toolbelt of technical and functional skills. The coveted data scientist operates at the intersection of programming skills, math and statistics, and business acumen, but a team of data science practitioners can also be assembled to cover this wide variety of skills. Consider if you already have a team staffed to fill gaps in what any one member may be lacking, or if outside professional help might be worth tapping into to make sure your data science implementation gets off on the right foot.
So you have the analytics culture and the technical chops. What’s missing? The data! Data science is obviously nothing without data, but it may not always be clear what and how much of it is required for your use case. Generally, if it would or could be considered in a human’s gut-feel decision, it must be collected and digitized in order for an algorithm to recreate or enhance your intellect.
Data science initiatives typically seek to build machine learning models, which are able to detect complex patterns by rapidly iterating on datasets, and massive amounts of data can help these models become robust to the subtleties of effective decision-making.
It might be worth considering an initiative in data quality or governance alongside your data science implementation to ensure that this essential input data is available and trustworthy.
Is data science the answer to your problem, or should you start with a simpler analytic solution?
Even without a pervasive trust in data or a savvy technical team, analytics is never out of the question for an organization. Data science operates on the predictive and prescriptive end of the analytical spectrum, where questions guiding your analyses begin with “How…?” and “Will…?”.
This forward-looking approach is capable of preemptively solving business problems before they even arise, but there is still much to be gained in the here-and-now by starting at a foundational level.
Consider beginning with a descriptive or diagnostic solution- think dashboards to provide a full view of your customer and intuitively visualize performance.
Alternatively, an organized data warehouse designed for easy querying of a customer’s lifetime of engagements with your company may work as a natural starting point.
Analytic achievements at this end of the spectrum are bound to build enthusiasm and increase accessibility of data over time if your organization isn’t quite prepared for a full-blown data science initiative, and may be better suited for your purposes, depending on the application in mind. Attempt to qualify your potential use case for data science by figuring out the core outcome you’re looking for in a solution.
Place the inquiries driving your solution on the spectrum above for a sense of where your initiative should begin. For projects on either end, start small and prioritize iterations of your use cases by assessing their business value and effort required.
Tackling the low-hanging fruit in your business can be a smart strategy for building confidence in your analytics journey and fostering the data-central attitude so essential to successful data science in your organization.
Whether you’re kicking off a complex advanced analytics solution or beginning with a first stab at visualizing your key performance indicators, data science, and analytics initiatives are nothing to be wary of. Just remember to take a comprehensive inventory of what is required to succeed, and consider which form of analytics is best suited to pave the road to a solution addressing your specific business challenges.
As always, if you need any help getting started, you can speak with a data science consultant right away at (813) 968-3238 or contact us at email@example.com. For additional information on our solution to build actionable machine learning insights in as little as 6 weeks, visit ccganalytics.com/Rapidinsight.
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