It’s no secret that data scientists are in demand.
These professionals leverage their understanding of the business’s needs to build models that solve specific problems. This advanced form of analysis requires extensive knowledge of machine learning, statistics, and the business data— skills outside the scope of a typical data analyst.
While these skill sets are essential for answering complex questions and making predictions of future outcomes, simply analyzing the data doesn’t help business people actually enact solutions.
Decision-makers need to understand how this analysis should impact their strategy, and ultimately, their actions. Thus, data scientists are also tasked with effectively communicating their findings to the business.
Herein lies one of the largest challenges facing data science teams today.
What are Data Science Unicorns and the Last Mile Problem?
The Last Mile Problem refers to the difficulty of making data science output actionable. While data scientists are experts at the analytical process, they often aren’t exceptional storytellers.
It’s incredibly difficult to translate complex insights into something business people (without any analytical background) can understand. Moreover, the ability to present and visualize data analysis is an entirely different skill set than building machine learning models. The rare employees who are able to accomplish both are known as data science unicorns.
A data science unicorn is fully capable of wrangling data, performing analysis, visualizing data, and presenting the findings to decision makers.
They’re so scarce yet in such high demand that hiring one, let alone a team of them, is unrealistic.
As a result, many companies struggle to maximize data science. Instead, they have The Last Mile Problem, with twofold results:
- Data scientists know they’re sitting on valuable insights, but they struggle to sell them to stakeholders. Decision-makers misunderstand or oversimplify the analysis, expecting the right answers to all their questions even when the answers are nuanced and complex.
- Executives don’t receive the guidance they need. They invest a lot of money in data science operations, but they don’t see tangible results— because the results aren’t communicated in their language.
In other words: frustration on both sides.
How can companies successfully leverage data science, knowing that data science unicorns are just as rare as their namesake? Simply hiring more data scientists won’t fill the gap.
Solving the Last Mile Problem (Without Data Science Unicorns)
First, let’s illustrate the data science process in more detail.
To perform the analysis, data scientists must:
- Gather and scrub data
- Plan and build models
- Test and validate models
- Evaluate models
- Deploy models
- Run models
As previously discussed, this is where most data scientists excel.
However, once the models are created, the data scientists find themselves stuck as the steward of that model. Every time the business wants to run the model on different parameters, the data scientist is pulled in to facilitate the rest of the cycle on repeat:
- Visualize data
- Form conclusions
- Present findings to decision-makers
This is where the Last Mile Problem takes hold.
The process itself is complicated, time-consuming, and repetitive. Data science teams can solve the Last Mile Problem and automate repetitive steps of the analytics process with augmented analytics.
Augmented analytics can simplify the entire workflow, helping both data scientists and decision-makers in the process.
Augmented analytics is the combination of machine learning and natural language technology to automate insights. Augmented analytics represents a collision of traditional business intelligence solutions and data science, allowing both producers of analytics and consumers of analysis to achieve their goals in a single workflow.
Simply put, augmented analytics:
- Enables end users to ask questions by typing them into a search bar.
- Selects the appropriate machine learning algorithm to perform analysis across the business’s data.
- Produces an answer in the form of visualizations and natural language insights in seconds or minutes.
- Automates insight production to create a continuous, proactive feed.
How does augmented analytics work? It employs similar “thinking” as the manual analysis process, but speeds up and scales up the work.
For example, decision-makers may want to know expected sales through the end of the year by week.
Just as a data scientist would perform a time series forecast, augmented analytics would do the same, choosing the model from a myriad of options (such as clustering, gradient boosting, or selecting a deep learning network). Augmented analytics understands that a time series forecast is the best choice to answer the question, and it automatically selects the model topology, parameters, and confidence.
Minutes later, augmented analytics delivers the forecast with visualizations and insights tailored to business people. As such, decision-makers can directly receive answers to business questions without needing to query a data scientist. This self-service analytics solves the Last Mile Problem for common business use cases.
Now, how does augmented analytics handle even more complex analysis or unique cases?
Data scientists can leverage openly-extensible platforms to deploy their own custom algorithms. Data science models are invaluable, created from extensive knowledge of business data.
Augmented analytics allows data scientists to input their models into the platform, developing custom workflows that can be performed with natural language queries.
In this way, business users and data analysts are given an approachable way to leverage these models to produce user-friendly visualizations and insights, without any data science technical know-how. This enables business teams to tap into advanced analytics capabilities—which they previously relied on technical resources for—all on their own.
Thus, data scientists can operationalize their machine learning models, automate steps of the analytics process, and allow business users to ask questions and get answers in their own language.
To sum up, augmented analytics makes advanced analysis accessible, approachable, and actionable by:
- Allowing data scientists, analysts, and business leaders to play to their strengths.
- Streamlining the most time-consuming, tedious portions of the data science process.
- Automating model running to enable proactive, continuous insights.
As a result, businesses gain the competitive advantage of speed. Faster insights enable decision-makers to act quickly, instead of reacting to change once it’s already happened.
Lastly, in addition to augmented analytics, it’s worth noting the value of Data Science as a Service (or DSaaS).
This solution allows companies to outsource their data science needs to a third party. A careful selection of a vendor can enable companies to tap into data science unicorns without having to hire them. It’s worth considering this option, especially for urgent business problems.
Do you have a business problem to solve with data science? Are you unsure where to start? RocketScience can help! Request a free consultation.
