Yes, there are real business applications for machine learning and artifical intelligence.
Some of the disciplines and experience required to hop in to machine learning, artificial intelligence, and other data science applications are so new that you probably don't have many specialists on your team. We can be the specialist with a harness for your jump into really big data when you are ready. Want to talk shop with a data scientist?
In some circumstances it makes sense to simply consume the output of a data science project within your application instead of investing heavily in developing the core capability on your team. If the line is fuzzy, then we can get you rolling and help you develop something that works while we train up your team. In other cases, we've been called on to extend a very large team of existing data scientists who need a platform from which they can accelerate their work. Enter the cloud - and our team of industry experts.
Let's try a simplified explanation for the sake of the space in this little answer box.
Some questions that require data to be crunched can be answered with a spreadsheet.
Other questions need a statistics heavy tool like SPSS or SAS.
Other questions that have very dynamic answers require custom models to be built in big data friendly languages like Python.
Other questions still have way to much data to be crunched in any reasonable amount of time on a single machine and require a bunch of custom development to create a neural network that leverages dynamic cloud resources to process absurdly large numbers of variables that could lead to a desired outcome.
Data science in the context of a business application is knowing what the right question is and what model and tools are required to answered that question confidently.
Data science as a discipline provides the skills and methods required to design and develop statistical models, machine learning models, and deep learning models. In a way, you can view a data scientist as the architect of systems that help us derive value from data insofar as the business supports the data scientist in asking the right questions.
Artificial intelligence (AI) could be viewed as a product of data science that relies on software engineering as a discipline in addition to data science as a discipline.
Machine learning sits in the middle of data science and artificial intelligence as a collection of statistical models that can derive a highly probable outcome based on a series of inputs.
We love this video from Mark Rober as he used several of these ideas to develop an app that steal baseball signs.
It is important to understand that in most cases data is indirectly monetized. It is easy to assume that data monetization only applies to businesses who are selling data directly, or to assume that we can only monetize our data if we sell that data in some way, shape, or form.
In most cases we find that businesses discover key questions and data-driven answers that might require some heavier lifting than the team has done in the past to uncover opportunities for indirect data monetization.
An example of data monetization could be in the insurance industry. Taking claims as a data set that has been used historically to predict internal risk, could instead be used as a data set to predict external risk for a cohort of customers. Answers to questions in that frame of reference could lead not only to a competitively advantageous marketing program that helps customers make decisions earlier in the insurance purchasing journey that will build significant trust and therefore higher conversion and retention rates; it may also lead to a more customer-centric culture internally that discovers more ways to strengthen long term trust with your clients.
Business transformations are typically centered around the adoption of a new technology or new way of doing business that can fundamentally change the value chain from your internal operations through to your customer experience. Data science, machine learning, and artificial intelligence are quickly becoming the common driving forces behind many business transformations in the last three years. The ability for organizations to develop software applications that can provide greater insight into operations, help there customers do more, and accelerate time to a business outcome has changed how leadership can think about different classes of business problems.
Outside of the design, build, and run aspects of a software development project, Veracity Solutions also offers a wide range of training and consulting services centered around how technology is used and developed within your organization. From data science to devops training, technology vendor assessment and selection, M&A due diligence and integration, and interoperability projects - we've seen a lot of gaps to be filled by our team and community of industry veterans. Not sure if we have experience with the road ahead of you? Let's schedule a time and an expert to talk through it.
"Startup" is a difficult term to define in today's world. We've worked with well funded high growth startups who are either selling a software product or who use software heavily to enable their business operations. We are always willing to have a conversation if you are not sure where to go. If it's not a good fit, we have some friends we can connect you with.
You've probably seen the Free Expert Hour buttons all over the website. An hour with one of our experts to talk through what you need help with will help both of us understand whether or not the partnership is a good fit. When you click the button you'll be taken directly to a calendar to book some time with us. We'll then have a team member reach out to make sure we get the right experts on that call.
If you can't find a slot in that calendar early enough for you, then just give us a call at +1 (888) 605-2266 and we will work out a better time.
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