Custom applications are exploding because of one simple fact: the entire world today runs on software and applications. We run software everywhere—on local machines, in massive data centers, in the cloud, and on personal devices.
Forbes has said that if you’re in business today, you’re in the software business. Forrester argues that a company’s competitive advantage is directly tied to how well it develops and delivers the software its business needs.
The good news is that you can reverse the cycle. Gartner says that optimizing your application development and maintenance can cut costs by more than 50 percent.
Speaking of custom applications, what do data scientists do?
Data science is simply the conversion of data to knowledge. Data may be structured or unstructured, and unstructured data can take many forms, such as text, images, or video. A data scientist touches on the use of data to help make business decisions or to analyze data and present it in a way that can help make business decisions.
Data scientists can have many different roles and responsibilities in the business. For example, they might:
- Educate the business – The business is often unaware that it has challenges that data science can solve. Part of the data scientist’s job is to explain how data science can help.
- Look for problems to solve – A data scientist who understands the business is in an excellent position to seek out and identify data-related challenges where the solution can bring value to the business.
- Research new techniques – New problems surface daily, and techniques are continually evolving to meet the challenges. Data scientists can be deeply involved in researching new algorithms and making known algorithms more effective.
- Collate data for analysis – This is often referred to as extract, transform, and load (ETL), the process of extracting data from different sources, transforming it into a format that makes it easier to work with, and then loading it into a system for processing.
- Crunch numbers – Data scientists must be well versed in techniques such as statistical analysis, exploratory analysis, and predictive analysis and can identify and apply the appropriate algorithms to the data.
- Implement algorithms – Many data scientists are software developers too, producing code that becomes part of a product. They are fluent in modern software development and delivery techniques.
- Design big data-capable architecture – The infrastructure required for data science is often different from the infrastructure for other types of projects. Data scientists need to be familiar with the state-of-the-art tools, many of which are open source.
- Present insights – The results of an analysis must be presented in a format that the nontechnical or non-mathematical person can easily understand and, more importantly, act on.
You don’t need to do it all yourself. There are effective ways to get the best of both.
In addition to cost-effective and high-impact custom applications development, we enhance your BI capabilities. Bring us into your strategic conversations.
As an experienced analytics thought partner, we’ll give you new insights into trends, measure progress toward a set of quantitative goals, flag shifts in your markets or financial operations and even lift your customer experiences to a higher level of satisfaction.
Call us to start a neutral and friendly conversation about your custom apps needs: 512-478-3848.