Choosing the Best Data Science and AI Solutions Provider for Your Business

Data Science and AI are the hottest topics now as far as business intelligence, and data-driven analytics are concerned. However, the so-called expert articles and talking on the television actually go far from the unusual. If not daily, the articles about data science and AI are coming up on LinkedIn and Facebook in plenty.

Due to this surge of information about big data and AI on the internet, the companies assume that data analysis is an essential solution for their data-related business management issues.

For businesses, it is important to plan for their AI and data science strategies most effectively to make use of these at best for their purpose. First, find out what technology you need and then identify a good provider who can offer you the best solutions in terms of tools and strategies.

Here in this article, we are putting forth various factors to consider while you are planning to search for a service provider offering data science and AI services for businesses.

Groundwork to be done

#1. Match your business issues with potential solutions

Primarily, AI and data science solutions are focused on getting actionable business insights with the help of the data available. Some enterprises may approach vendors for data science to learn about the market, build their products or services, by setting their key functionality centered around the machine learning algorithms.

For example, some may be focusing on ML applications which can transform speed to text. Another organization may be working on some visualization tools to control their operations and make more strategic decisions based on the insights gained.

In a broader sense, one can make use of data science to get a better insight towards the business to enhance the efficiency of their operations. Some may want to deliver applications which can offer AI-based solutions to the uses like face recognition or so. So, the end customer could be another business, or in some cases, it can be the customers.

The modern-day customer-facing apps which are powered by AI and machine learning can effectively solve the user problems. In the coming day, people may be using these applications largely in their daily life to do the works easier and faster.

Below are some of the customer-facing applications as offered which needs data science and AI engagement?

• Virtual assistant for text to voice like Mezi for travel assistance and Expedia chatbot etc.
• Recommendation engines used by eCommerce portals to provide the most matching products to individual users.
• Examples are the personalized recommendations systems of Netflix and Amazon etc.
• Price prediction engines which could analyze the historical data and make a prediction of commodity prices like Kayak or Fareboom fare predictions etc.
• Speech to text apps like IBM Watson and QuanticApps Voice Assistant etc.
• Analytical applications and Sound recognition apps like the Do I Grind app for healthcare etc.
• Image editor applications like Prisma app.
• The image recognition apps and search engines like Uber app and credit card recognition etc.
• Live voice and visual transcripts and translation services like iTranslate Voice or Google Translate etc.
• Applications for document classification like Knowmail.

There are also a large group of applications for fraud detection, which employs both conventional programming methods and data science techniques for business analytics and intelligence.

#2. Business Analytics vis-à-vis statistical analytics

BA or Business Analytics is exploring data with various statistical and analytical operations. The objective of BA is to keep a close watch of the business processes to derive insights that can be used for informed decision making.

Primarily, the BA techniques can be categorized into two primary groups as business intelligence as well as statistical analysis. Enterprises using business intelligence will analyze the historical and then try to gain insights from the past events which allow the companies to take strategic decisions and further developmental options.

On the other hand, Statistical Analytics or SA is the processes of digging more deeper into the data with a problem in hand. For example, you can use statistical analytics to find out why customers tend to book from a particular OTA rather than from the website of the hotel or whether or not the users buy a particular product after reading a particular mail about the special offers, etc.

BA is largely used for purposes like:

• Data and database management
• Scorecard and dashboards development
• Big data analytics
• Sales, price, market demand forecasting
• Customer behavior and client data analysis
• Social media analytics
• Sentiment analysis
• Risk analysis
• Market segmentation and customer value management
• Life value prediction of customers
• Opportunity analysis for upselling etc.

Business analytics now let application users solve even complex problems from small to large businesses alike and also help in operations improvement. To address such problems, you may try to make use of four types of analytical methods like descriptive, predictive, diagnostic, and prescriptive, etc.

#3. Considering off-the-shelf analytical products

Before planning to set up a team for Data Science and Business Analytics, you must first explore the options of off-the-shelf data science solutions too. The provider sites like PCMag and KDnuggets etc. have listed a fair collection of analytical solutions and SaaS-based business analytics service providers.

There are many among them which specialize in software research too. Well supported off-the-shelf solutions are not only highly useful but also largely cost-effective when it comes to small to medium size business management using analytical tools. Along with BI and AI, the providers also now offer add-on modules which can take care of your end-to-end business needs.

However, there are some drawbacks also for off-the-shelf analytics solutions as these may not sometimes support the entire functionality you need. Say, for example, if you have to adjust the marketing campaigns based on your needs to analyze the customer reviews or shares on social media, many of the analytical platforms may not support these features.

In such cases, it is ideal to hire a good team of data science specialists and analytical professionals to form a tailor-made team for your business intelligence.

Wrapping up

The abundance of data science and AI specialists won’t confuse you if you have a clear project objective and at least an essential and basic understanding of what sort of skill or expertise you have to accomplish it.

• So the main thing you have to do in order to find the incredible team is making sense of what AI and Data Science specialists actually do. Simply discover some time for exploring terminology, because knowing it is really essential.
• The second stage is clarifying your personal target for contracting new workers. Match your concern with possible solutions, Consider off-the-shelf products and Study organization postings.
• The third task for you is concentrating and studying the key standards of spotting high-potential employees. Here you should explain questions like: Do you have the necessary human capital? Does the advisor have the necessary experience?

To wrap things up thing is employing winning recruitment algorithm which is consisting of a technical interview and HR and Personality interview with Team Leader.

About the Author!

Pete Campbell is a social media manager at blastup who has worked as a database administrator in the IT industry and has written numerous articles and blog posts on topics related to DBA services for small businesses. Learn more about hiring the best experts at RemoteDBA.com. He loves to travel, write and play baseball.