Data science can be used to learn about habits and processes, create algorithms that rapidly and efficiently handle enormous volumes of data, improve the security and privacy of sensitive information, and assist data-driven decision-making. Knowing how to make sense of data, the terminology needed to traverse it, and how to use it to create a good influence can be vital assets in today’s corporate world. Modern enterprises are saturated with data. According to a recent study, McKinsey estimated that big data initiatives in the US healthcare system “could account for $300 billion to $450 billion in reduced healthcare spending or 12 to 17 percent of the $2.6 trillion baselines in US healthcare costs.”
Data science is all about using modern techniques to answer business questions that traditional BI techniques cannot answer.
In today’s blog, Tallan Director of Data Science and AI Lee Harper will discuss what questions businesses should be asking when it comes to empowering their decision making with data science.
What is the role of data science in a digital transformation?
Data science is all about using modern techniques to answer business questions that traditional BI techniques cannot answer – particularly around unstructured data (images or text, for example) or predicting something that may happen in the future (predictive analytics).
So, where does this fit into a more comprehensive transformation? It’s when your business is ready to start using data in very different ways from how it may have been done before, and as such, can be genuinely transformative to how you do business. For example, to automate certain processes that may have been manual, time-consuming, and error-prone such as inputting financial statements from PDFs into excel spreadsheets or databases, thus freeing up resources to do more valuable work. Or, to forecast regional demand over the next six months for your products and services to enable you to make logistical decisions and better target your sales and marketing strategy.
It’s also worth mentioning that, although data science is most effective when combined with a robust modern cloud data platform, this isn’t a prerequisite for using these powerful technologies – you can still introduce some effective data science in an on-premises or desktop environment to start gaining this transformative benefit today.
What are some common barriers stopping clients from implementing data science?
A prerequisite for data science is found in the name – we need the freedom to access a range of data sources in order to do data science! Many organizations have, often for good historical reasons, highly siloed data, which can prevent us from effectively assembling what we need to be successful. For example, if I’m trying to build a model to predict cross-sell opportunities, I may need data from your product catalog, your CRM system, your website visits, your call center and others to be available to me. If this isn’t possible, then it may not be possible to build the model you want. If you’re willing to break down those barriers, though, then a data science initiative can be an excellent catalyst.
Another barrier – and honestly the most common failure point of DS projects – that many data science teams encounter is around the deployment and productionalization of their machine learning system. Most data scientists are excellent researchers and analysts, but many lack software and DevOps engineering skills. This means it can take months or even years to enable the enterprise to use its system, which is where the ROI is. This leaves some organizations rightly questioning the benefits of their data science program. This has led to the introduction of the “MLOps” field, which is the hottest topic in the ML field – an area in which Tallan has significant expertise.
You can still introduce some effective data science in an on-premises or desktop environment to start gaining this transformative benefit today.
Finally, given the significant mathematical and statistical content, you need to be very intentional with communicating results to business stakeholders who usually care more about the added business value in dollar terms. Many data scientists find this “data storytelling” element challenging, and this friction can inhibit the enterprise adoption of successful data science solutions.
Tallan’s Data Science & AI practice was created to optimize your overall operation by combining experienced programming and modern technologies. Tallan’s experts can create and deploy models to help with predictive maintenance and analytics, as well as supplement your team as needed. Click here to learn more!