Businesses across the globe have started adopting artificial intelligence and data analytics to streamline their operations. Investing in the latest technology helps enterprises optimize resources, increase productivity, improve quality and get a faster return on investment.
From following the traditional approach of decision-making, SMEs are slowly turning into data-driven businesses. Big data is being stored, processed, and analyzed to gain actionable insights. These insights are gathered from historical and real-time data to help the management make faster and better decisions.
However, doubts and confusion about AI, ML, and data science still persist. The concepts overlap and coexist. That makes things a little hard to understand. Artificial intelligence consulting companies have been helping SMEs understand how AI, ML, and data science are different from each other.
The companies provide continuous assistance to revamp the business processes and build an efficient operational system in the enterprise. The aim is to strengthen the business in all aspects to achieve its long-term goals.
What is Artificial Intelligence?
One thing most of us do know is the generic definition of artificial intelligence. Minsky and McCarthy, in the 1950s, defined AI as a task performed by machines that would usually be handled by humans.
But artificial intelligence is much more than a machine taking over the tasks of a human. AI is a collection of algorithms that help computers understand the relationship between various entities, gather meaningful insights, or plan a course of action for the future. AI is all about actions. Machine learning and deep learning are a part of artificial intelligence.
The machine-learning algorithm can learn and improve itself without the intervention of humans. Have you read about AI solutions like Chatbots and virtual assistants providing personalized assistance to employees depending on their responses to the questions?
It means that the machine learning algorithms use data in the system and the response of the employees. The algorithms learn from the feedback and deliver better accurate services.
Types of AI
Artificial intelligence is classified into different types based on functionality and technology.
Based on Functionality:
- Reactive Machines: These are the oldest AI type of AI systems with limited capabilities. They do not execute memory-based functions but are used to respond quickly to typical input datasets.
- Theory of Mind: These AI systems are supposed to understand the human mind, emotions, feelings, thoughts, etc., and identify the factors that influence the human thinking process.
- Limited Memory: These AI systems learn from previous data and are the ones we commonly see in today’s world. A vast amount of data is fed to train these systems.
- Self-Aware: These AI systems are yet to be fully used in the market. Some of these are still in developing stages. These systems are meant to have the self-awareness and consciousness of a normal human being.
Based on Technology:
- Artificial Narrow Intelligence (ANI): Also known as weak AI, this is the common type of AI technology we see in the industry. The systems use a predefined set of constraints to process data and deliver results.
- Artificial General Intelligence (AGI): This technology is connected to Theory of Mind and has been fully developed as yet. The aim of this technology is to develop machines that create independent connections across various domains.
- Artificial Super Intelligence (ASI): This technology is still in the early stages of development and can be linked to Self-aware AI systems.
Why Use AI for Future Growth
According to Fortune Business Insights, the global AI market value is expected to touch $267 billion by 2027. AI-powered robots and machines are going to become an integral part of most industries, be it manufacturing or service and hospitality sectors.
Investing in AI at this stage will ensure that the organization is ready to adapt to the changes in the future market and grab more opportunities.
How Do You Develop an AI Strategy? (AI Strategy Framework)
- Developing the AI strategy framework requires proper understanding of the business and its goals.
- The first step is to start with the AI use cases so that it becomes easier to pick the most important ones.
- Identify the necessary technology and skills required to use AI.
- Does the enterprise have a data strategy? It’s time to develop one now.
- There are certain ethical and legal considerations that need to be cleared. Data privacy, bias, discrimination, etc., are some examples.
- Start building/ adopting the technology and infrastructure required to use AI.
- Close the skill gap by hiring new employees, training the existing ones, or relying on offshore experts.
- Implement AI projects stage-wise.
- Simultaneously, make changes to the work culture so that employees find it easy to adapt to the new changes.
- Keep a track of changes and results to ensure that the goals are being achieved.
What is Data Science?
Data science is a broad field of science that deals with collecting, cleaning, storing, processing, and analyzing data to derive meaningful insights. The techniques of mathematics and statistics are used along with advanced technology and tools to derive useful information and knowledge from vast amounts of data.
Data analytics, data mining, and data visualization are a part of data science. AI and ML are used in the data science field to produce insights that help in better decision-making. Hiring the leading data analytics company in India provides the necessary skills and technology required to gather raw data and process it to detect hidden patterns, identify market trends, and predict future outcomes for the enterprise.
Data scientists and analysts use AI and ML tools to work on chunks of data (historical and real-time) to draw inferences and generate reports to explain the insights in a simple and easy-to-understand format.
How Do You Develop a Data Science Strategy? (Data Science Strategy Framework)
Similar to AI strategy framework, data science framework also needs to follow a systematic process to yield the expected results.
- Start by identifying the key business drivers
- Build an effective data science team
- Focus on communication and collaboration between members and teams
- Make use of data visualization tools to present data in attractive formats
- Let the data science teams access all the data found in the enterprise
- Develop a data science process to operationalize analytics
- Develop new and better governance policies to protect sensitive data
Relationship Between Data Science and Artificial Intelligence
Human intervention is required in data science to process and analyze huge amounts of data and derive actionable insights. Data scientists build ML models that can be implemented in the business to make the most of the data that’s available in the enterprise to forecast business. The following are the two popular ways in which consulting companies use data science in business development.
Predictive Analytics: Data from the past and present are used to analyze trends and patterns to predict the future outcomes for the business and in the market. This helps enterprises be ready to grab new opportunities in the market and make the most of the latest trends. Organizations can also be better prepared to deal with market fluctuations. Predictive analytics is used to avert a crisis instead of finding a way out after it has occurred.
Real-time Analytics: This is a type of machine learning where any anomalies and deviations in the present model are detected by comparing it to historical data in real-time. It helps enterprises get a clear understanding of how things are different (or the same) when the present situation is compared to the past. Real-time analytics is very much related to big data’s dimension of velocity where data is being generated rapidly and we need to analyze the data on the go.
Is Data Science Required for Artificial Intelligence?
The short answer is yes. Artificial intelligence is a set of mathematical algorithms that help a machine to understand and process data. Data science, as we mentioned above, is a broader field that includes mathematics, programming, and domain knowledge. An AI Engineer who understands the basic concepts of data science will be more efficient.
How to Build Data Science Capabilities?
Building a data science team needs time and effort. That’s one reason why SMEs prefer to hire the services of a data science company in India. Using offshore consulting services is a cost-effective solution, that too, without compromising the quality of services or the expertise of the professionals.
But here, we are talking about the importance of data science in business and what you need to consider when building the required capabilities to empower your employees.
- Identify the Key Factors
You should know exactly why you are investing in data science. Why does your business need more than what the traditional BI tools can offer? How will data science increase your competitiveness in the market? Which areas will be your major focus? Do you want data scientists to focus on a single department, or will it be across the enterprise?
- Build an Eligible Team
Once you have answers to the questions in the previous point, the next step is to bring together experts in the field to create an efficient data science team. One crucial decision you need to take here is whether you want to outsource or build an in-house team. For that, you need to understand the existing skill gap in the talent pool. Cost is another factor that plays a role. Would it be feasible to hire experts from various domains to build a complete data science team? Can the same be achieved faster and for a lesser cost by using offshore services?
Of course, you will still have to train your employees to use the insights provided by data scientists. Your existing teams should work with the data science team to increase the overall efficiency of the enterprise. That leads us to the next point.
- Create an Internal Communication Channel
For the data science business ideas to deliver successful results, the communication channels in your enterprise need to be open and flowing. If there is no proper interaction between the employees from different departments, data science isn’t going to help much. Assumptions, beliefs, and misconceptions can cause a lot of trouble in any enterprise. Your teams need to talk to each other. They need to collaborate and work together to achieve the common goals for the enterprise. Two things that make collaborations successful are communication and access to data.
- Eliminate Barriers to Access Data
Data scientists must have access to the entire data available with the enterprise. The precision of the models created by the data science teams depends on the data provided to the teams. While traditional business intelligence systems provide structured and processed data, that alone is not enough to get in-depth insights. Data scientists work with raw data as well as processed data. Only then the team can identify the unseen patterns and unlock the power of raw data.
Similarly, other teams in the enterprise should also have access to the insights provided by the data science teams. The reports should be shared with every employee who has decision-making responsibilities at work.
- Focus on Ethical Use of Data Science
This is the trickiest part of the process and one that has been under debate in recent times. Data science strategies involve advanced analytics. Though it helps you deliver highly personalized services to customers, you need to consider things from their perspective as well. Customers are wary of how enterprises use their data and share it with others in the name of personalized services. Also, the increasing cases of security breaches and data leaks are causes of concern that need to be addressed. When developing the data science capabilities of an enterprise, you need to give equal importance to data security and data privacy.
Importance of AI and Data Science Capabilities for Strategic Growth
There are several applications of data science in business management. The escalation in demand for qualified and experienced data scientists is an example of how data science is gradually becoming a part of SMEs and multinational organizations.
- No Use of Data without Science
What will you do with loads and loads of data if you don’t know how to process or analyze it? Many businesses collect data without really knowing what they’ll do with it. You don’t want to join the same gang, right? Science gives you a solution, a method to understand incomprehensible data. This allows you to actually make use of the data you’ve been collecting over time. Data is pretty much useless on its own unless it is combined with science and technology to derive insights.
- Use Data Across the Verticals in the Industries
The reason we emphasize so much on data is that it has valuable information hidden in it. Artificial intelligence, data science and business analytics are not limited to a single industry or a specific department in an enterprise. From healthcare to education to retail, banking, and manufacturing industries, data science capabilities can help an organization make full use of data scattered across the business. A data scientist can work in any industry with any enterprise.
- Improve Customer Experience
Whether it is products or services, customer experience determines the ultimate success of the company. The business volume doesn’t matter either. Data science for small business is just as necessary as data science for a large enterprise. Data science uses machine learning algorithms in eCommerce to provide personalized product suggestions to each customer. It helps plan the movement of inventory and release of products in the manufacturing sector to suit customer demands.
- Building a Data Backbone in the Enterprise
Data is termed as the future of the world. For an enterprise to survive and sustain itself in the competitive market, it is essential to build a strong base for its future. This is done by developing a data-driven model where the decisions are made based on factual information rather than guesswork. Also, building a stronger data backbone in the enterprise will equip the management and employees to be ready for the challenges that come their way.
- Make Better Decisions
Predictive analytics is one data science strategy that helps enterprises to make informed decisions in a quicker time. And this is not limited to the consumer market alone. Data science can be used in the banking and financial industry to identify trends that could lead to losses. Artificial intelligence and machine learning algorithms help in fraud detection and prevention. Factors that previously seemed unrelated are now identified as having a significant impact on each other. This helps in understanding the core of the problem and makes it possible to find the right solution.
- Automate Recurring Processes
Automation is one of the most familiar applications of AI. Artificial intelligence consulting firms help in identifying the key areas where automation is necessary for the enterprise. Any repetitive work that can be performed faster by a machine should be automated to save time for the employees. This gives the employees more time to work on the actual project and improves their productivity.
- Empowering Employees
Continuing from the previous point, data science capabilities empower employees with valuable insights. When employees have access to the latest data and know-how a certain aspect could affect the project, they will be prepared to work around the potholes and successfully complete the work. When For example data science empowers the is used by the HR department, it gives by providing them with a better picture of the employees’ strengths and weaknesses, resulting in building stronger teams and designing training modules to improve employees’ skills and knowledge.
- Impact of AI in Market Research
There are several advantages of using artificial intelligence in market research. Firstly, market research will be cheaper and faster, thanks to AI. By using real-time data from several sources, the insights shared by the AI-powered tool will be more accurate and useful in making decisions that can impact the customers’ purchase behaviour.
Conclusion
Data science helps us understand what raw and unstructured data is about and how it can be used to optimize the resources and performance of the enterprise. With artificial intelligence and data science becoming must-haves in every industry, businesses need to increase their adoption speed. Contact a well-known analytics company in the industry to ensure that your business gets the right kind of support to streamline business operations and build a data-driven process. Discover and unlock the power of data to grow your business.
– Kavika is Head of Information Management at DataToBiz responsible for the identification, acquisition, distribution & organization of technical oversight.
