Data Science as a Service: Add value to your company

Big data is no longer just the numbers we collect. Due to the advances in our computing abilities, it has transformed into value today. Machine Learning (ML) algorithms, along with other data analytics tools, help us convert Big Data into meaningful insights that can be used as business intelligence.  

In the recent past, Data Science as a Service is an often-heard term. How does DSaaS relate to the analysis of big data or Machine learning? How do DSaaS companies benefit various businesses?  This blog article explains the concept of data science as a service and later discusses why partnering with DSaaS companies is favorable for most businesses.

In order to optimize business processes, many companies need to use predictive modeling. For instance, in manufacturing plants, data related to various parameters such as temperature, pressure, and properties of the raw material are constantly collected. The quality of the final product depends on carefully monitoring these parameters. The manufacturing plant needs data analysis algorithms or ML algorithms that optimize production and reduce waste.

Data science companies help convert Big Data into insights

At present, there are two options for developing data analytics capabilities. Some businesses hire a data scientist (or a team of scientists) to process the data. Most teams start small with one or two data scientists and gradually add data scientists as the projects advance. Team nurturing happens over time. Some businesses go on a hiring spree and try to build a big team. But they struggle without the right tools and fail to produce tangible results. Furthermore, skilled data scientists are much-needed by the industry. So building an in-house data science department is expensive and retaining skilled data scientists may not be easy. For companies operating on a limited budget, this option is not the best one. 

Another option, some companies partner with a data science service provider or DSaaS company such as BitRefine. It allows companies the flexibility to engage with DSaaS companies on a project-by-project basis or long-term basis. Furthermore, the client companies who have a data science department also collaborate with DSaaS companies when a specific skill-set (e.g., predictive modeling) is required for short duration.

Data Science as a Service is a form of outsourcing where DSaaS provider collects data from the clients and delivers algorithms or meaningful insights from the data. One of the most important and intensive operation is data cleansing. Poor quality data has an adverse impact on final results, hence upon receiving the data, provider company first prepares data, looks for missing values, constants etc. Then DSaaS companies develop algorithms and deliver the results or algorithms to their client. In addition to algorithm itself, DSaaS provider also offer consulting services regarding good data governance practices.

DSaaS companies provide results that have the potential for changing the business processes. Traditional business analytics is less sophisticated and thereby often slow. DSaaS companies build tools that are easy to deploy, provide analysis in real-time and from that strategic analysis, the clients can identify the next steps for the business process.

Data science consulting is an essential part of DSaaS

Finding a right DSaaS provider can be a challenge. A client needs to carefully evaluate values of a DSaaS provider prior to engaging with them. A good DSaaS provider focuses on the end user. Instead of taking pride in developing a complex algorithm, a good DSaaS provider develops tools that are useful for the end user. A great DSaaS provider is proficient in data science and also knowledgeable about the whole data governance structure. An excellent provider is ready for a tight collaboration with the clients’ experts who can share business or manufacturing process expertise. She then consults in transforming the business, as a data-driven client company is managed in a different way. It enables the client to transform data into actionable insight. 

It is estimated that by 2019, predictive modeling itself will be over $5 billion industry. This will result in trillions of dollars cost-saving for the manufacturing sector, along with other sectors such as oil, petroleum. Even for small businesses, the time to engage in predictive modeling efforts has arrived. Finding a right DSaaS partner is the next step towards success.