Artificial Intelligence and data science are hot topics in the media. This expert isn’t uncommon for TV shows that regularly cover predictive analytics for business. Facebook and LinkedIn both use AI or data science to keep us engaged, make their products better, and create a better experience for their members. Big Data, Artificial Intelligence, and Cloud Computing are becoming increasingly important aspects of the modern workplace. As a result, companies assume that data analysis answers many of their data-related issues.
There is a company that uses data from the Innovata dataset to make decisions. It contacted our data science team with a request to analyze it to extract data. The FlightGlobal news and information site is a leading provider of historical, current, and future schedule data for more than 900 airlines worldwide. Our clients were concerned about working with large datasets as they felt it was a big data problem.
Match Your Problem with Possible Solutions:
Data science and AI solutions generally entail gaining valuable insights using available data. Some companies approach data science vendors to build products whose key functionality is centered on machine learning. For instance, this may be an application that transforms speech into text. Other organizations may want to develop a custom analytical and visualization platform to be in control of their operations and make strategic decisions based on the insights.
In the broadest sense, you can apply data science to gain insights into your business and improve your operational efficiency, or you may want to deliver AI-based applications to your end customers. In the former case, the end consumer would be your company. In the latter – your customers.
Business Analytics: Business Intelligence and Statistical Analytics:
The Exploration of Data through Statistical Analysis and Operations Analysis. BA is a must for business analytics. Business analytics uses data for making informed decisions. BA aims to monitor business processes and use insights from data to make better decisions. There are two kinds of business analytics: Business Intelligence (BI) and Statistical Analysis (SA). Business intelligence experts analyze and report on historical data for companies. They also develop new models to help executives make smarter decisions.
History can help companies make strategic decisions about current operations and development options. Statistical analytics allow for digging deeper while exploring a problem, and they are useful for getting a better understanding of what’s going on.
Business Analytics can be Used for:
- Dashboards and scorecards development
- Price, sales, or demand forecasting
- Sentiment analysis on social media
- Market and customer segmentation
- Upsell opportunity analysis, etc.
- Data management
- Big data analysis
- Client analysis
- Risk analysis
- Customer lifetime value prediction
The rise of business analytics allows solving problems of various complexity, from simple reporting to advice on risk mitigation or operations improvement. You need to address these problems by using a variety of analytics. There are four analytics types: descriptive, prescriptive, diagnostic, and predictive.
Each company should strive to prove it has sufficient expertise in its field. It’s easy to make a sales page with nice-looking testimonials and logos. But case studies that come from real clients are more effective than testimonials. A case study gives insight into a client’s background, the technology and methods the data science consulting company applied to solve a specific problem, and the outcomes.
The level of analytics used in case studies is another source of guidance for a consultant search and selection. Check out previous clients, and if you want to get them to refer you, ask them to contact the company and give you a testimonial. To find more leads, you should call companies interested in what you offer.
Return on investment (ROI) estimation
Having a solution to a challenge isn’t enough. You must find an investment that pays off within a set time. This is a key task for a vendor team, but it’s only one of the key tasks. Imagine that your company loses a significant amount of money due to fraud.
If you have a list of IP addresses previously used for fraud, you should use it to buy a list of other IP addresses. Off-the-shelf software is a very effective solution for combating fraudulent activity. It can prevent fraudulent activity by blocking certain parts of a transaction.
The number of data science and AI consultants will not confuse you if you have a clear project goal and at least a basic understanding of what kind of expertise you need to achieve it. At the same time, you must research whether the problem can be solved using DS or AI. A consultancy must have a scope of work defined, perform a preliminary evaluation, and assess the return on investment for the project.
Some additional factors to consider before making a final choice:
- Data science team training. It’s an important decision whether to use a vendor that shares its knowledge of building data science teams.
- It would be best to be an expert in other areas besides modeling. In my case, I’m a musician, and I have an online store, and these skills helped me to get the project off the ground.
- Discuss partnership details with a potential consultant.
A team following the Lean project management framework requires the entire length of the project to be spent in close contact with the development team, so your representatives must attend every meeting or phone call.