Does your data science team enable your ROI or is it just another cost center? Do your boardroom discussions revolve around how analytics can help drive company strategy? If not, then probably the data science team is simply a research and development department. The problem is not your brilliant team, the problem is your team does not have the right platform for data science work.
Let’s look at how data scientists can contribute to solving real-life business problems, as well as how the Teradata Vantage platform can help data scientists enable a better ROI for their company.
Use 100% of Your Data
To create data science models that solve real-life business problems in an optimal way, you need to utilize all of the data. For example, take the use case of asset failure prediction. Large enterprises have assets which directly impact our day to day life. An electricity distribution company has assets such as power generators, high-voltage transmission lines and sub-stations which bring electricity to our homes. A telecommunications company has assets such as cell towers, fiber optic cables and antennas that help people communicate and use their phones. Any malfunctioning or failure of these assets can have a huge impact such as power cuts, explosion and communication network unavailability in an emergency. These kinds of events can lead to customer loss, as well as litigation and public relations disasters.
Asset failure prediction helps anticipate the failures of equipment before they happen. To make more accurate predictions, it’s important to utilize all of your data. One might believe that failure is dependent upon how old an asset is and therefore maintenance-based on asset age data is sufficient. However, many times asset failures can depend upon a variety of factors, including environment, weather, temperature, vibration, the assets it’s connected to and the number of customers it serves. Therefore, using all the data such as age, past failures, connectivity, weather, load and information gleaned from sensors can give an organization insight into when assets are near failure.
With Vantage, you have the possibility to use 100% of your data. Vantage allows data scientists to connect to different data sources and run analytics in a scalable way. This will lead to models which can predict asset failures with higher accuracy preventing the associated toll of catastrophic events. This will enable data scientists to directly contribute to a company’s financial success and reputation.
Avoid the Analytic Siloes
Most of the data scientists are trained to solve problems at small scale. The usual way is to download data on local machine or spin-up a large memory server and use languages like Python or R to do the data science work. This approach works well for academic working, participating in data science competitions, or hackathons.
However, this creates analytic silos where you have parts of data spread across the enterprise. The most dangerous place for data is on a data scientist's local machine. As data scientists are used to connecting to open-source and external websites, this leaves them open to an increased risk of data hacking and exposure.
With Teradata Vantage, scientists will never have to download data onto a personal computer or extract data to another machine. Vantage provides the possibility to connect to different data sources and provide a single point of access to data scientist. This helps avoiding the analytic siloes and helps businesses become more productive in their data science and analytic work.
Solve a Wide Variety of Business Use Cases
For companies to leverage the advantage of data science, multiple business use cases need to be taken up by data science team. For example, a telecommunications company will require a customer churn prediction use case to predict whether a customer will leave or stay. They will also require voice of customer use case which analyses what customers say about the company on various social media channels. Many telecommunications companies also provide business to business services such as logistic fleet management. Offering logistic and route optimization capabilities is a very important use case for these organizations.
To handle such a wide variety of use cases, multiple data science capabilities and algorithms are required. A churn prediction will require predictive algorithms, while voice of customer will require sophisticated text analytics in multiple languages. Logistic and route optimizations require graph algorithms which help in selecting the optimized route between two locations.
For a data scientist to work in an efficient way, it is important to have all these different kinds of algorithms in the same platform. We call this multi-genre analytics. This helps avoid having different platforms for different use cases.
Teradata Vantage brings all these different algorithms
to the same platform. It’s the platform for Pervasive Data Intelligence. Vantage is the only platform of its kind packed with algorithms that are capable of managing all of the data, all of the time, to answer the toughest business questions. With Vantage, data scientists in your organization have a true opportunity to better business outcomes and contribute to ROI.
The output of data science work is generally called models. Putting it in a very simplistic way, a model is a mathematical formula. These models correspond to a use case. For example, you have models which help detect customer churn and another model to predict asset failures.
In order to have business benefits and ROI from data science, the company needs to operationalize these models on regular basis. Predicting customer churn or predicting asset failures should not be a one-time activity. Operationalization means using the data science models on a regular basis for business benefits.
However, operationalization is the single biggest hurdle that data scientists face. One of the main reasons is that the platform that data scientists work in and the operational platform they use, are usually not the same. Therefore, data scientists have to find a way to convert the models into format which is understandable by the operational system. This conversion process, means all the work done by the data science team gathers dust by staying in the research and development phase because it’s never used on a regular basis.
The reason for the disconnect, was there was no easy way to put the data science models in operational system. Until now. There is no need to convert models into specific formats, Vantage provides a seamless way to use the models developed by data scientist. Whenever, the data science team develops a model, it is stored in Vantage tables and is available to be used on regular basis. Now data scientists can analyze anything, deploy anywhere, and deliver analytics that matter for their organization.
With Vantage, data scientists can contribute to your company’s ROI. When organizations can operationalize analytics on an enterprise-ready platform, they are able to drive crucial business outcomes far ahead of demand.