By now, you already know how critical data is to the success of a business. Data can provide insights into market trends, customer behavior, and opportunities for growth. However, companies need a reliable strategy for collecting and analyzing this data. Without the right people, tools, algorithms, and processes, a data collection strategy can become costly and ineffective. Collecting and storing large amounts of data comes along with governance and compliance considerations that if not addressed can result in fines.

Protecting privacy and maintaining the security of consumer data is an essential component of data processing. So how can a business keep up with the Wild Wild West of data gathering, analysis, and protection? Artificial Intelligence (AI) is the answer.

AI and machine learning provide businesses with the tools to keep up with large amounts of data. By automating essential data functions, a business can develop a strategy for sifting through large amounts of information and getting as much value out of it as possible. Data scientists are critical to any data analysis strategy. Not only do they help develop and deploy algorithms that facilitate data processing, but they can also help a business keep up with the rapidly evolving Governance, Risk, and Compliance (GRC) environment.  With the proper tools and talent in place, a business can navigate challenges, gain more insights from data, and turn data processing into specific steps that help it achieve goals and identify opportunities.

The Role of Data Analytics in Company Operations

Data analytics can help fuel a company’s operations in many different ways. For example, it can provide a highly customized shopping experience for customers by keeping up with what they want. By analyzing purchase history, demographics, and other related variables, this data can be used to develop a customized profile for each customer. Furthermore, data analytics allows a company to control inventory costs, identify new markets, and even boost employee performance.

The use cases of data in today’s world are endless. However, many companies don’t have the infrastructure necessary to leverage a data analytics approach. They may lack tools for data collection and processing or the headcount to design workflows for data analysis.

By combining the right infrastructure with data mining and statistics, a company can gain better insight into data. Machine learning is a subset of AI, where a data scientist can program automated functions using insights generated from previous data. This is how companies can detect fraudulent transactions, recommend products that customers love, and even recognize user faces. AI and machine learning are at the top of the data science hierarchy of needs.

It starts with having a strategy for collecting data, storage, transformation, and optimization. In other words, raw data collected from customer purchases, equipment sensors, online activity, and other sources will need to be put in a more usable form. This is when algorithms can be deployed for machine learning.

The primary challenge that most companies face is navigating the data science hierarchy of needs from start to finish. A company’s journey will vary depending on how much data is being processed, what form the data comes in, and its overall objectives.

Implementing a Plan for Data Analytics (Recruiting Data Scientists)

The use cases of data analytics have driven many companies to seek services from data scientists. Indeed, having the right talent on a team can help a company reduces costs, maximize returns, and identify new opportunities. But finding data scientists sounds easier than it actually is. Demand for such positions has risen significantly since 2017 and it is a global phenomenon.

Data scientists are the heroes who will drive a business’ analytics approach. They help develop and install tools that help a business make better sense of data silos while working with other departments to develop strategies for acting upon these insights. Due to the shortage of data scientists in the market, companies have had to develop an effective strategy for leveraging business data while recruiting top talent.

The first step is thinking ahead. Determine what the central goal of the data strategy is, and develop a roadmap for pursuing the goals. A company doesn’t need highly expensive or futuristic tools to start leveraging business data. In fact, the data science hierarchy of needs starts with basic data collection. This involves instruments, sensors, external data sources, and user-generated content. The need for data science has resulted in many tools available to help a business start on the path to data analytics, including software that contains data mining features and other statistical tools.

The next step is hiring talent that can fulfill specific roles within an organization. The official name of the position is less relevant when compared to assigning appropriate responsibilities towards business goals. Therefore, focusing on creating positions that are tailored towards solving specific problems typically helps build solid teams. This is a team effort where a company may have multiple personnel working together to address challenges the analysis identified.

The effect of AI on GRC

Data analytics in general (and AI in particular) has a significant role to play when it comes to Governance, Risk and Compliance. The oversight environment is becoming increasingly complex as businesses collect larger amounts of data. Furthermore, cybersecurity threats targeting personally identifiable data are growing by the day. Without a reliable GRC strategy, a company may be vulnerable to hacking and costly fines from regulatory bodies. The good news is that AI can be used to streamline GRC functions.

Companies are using machine learning to detect and respond to incoming hack attempts while implementing workflows that fall in line with guidelines from regulatory bodies. For example, risk assessment calculations can be automated, data storage and transportation channels can be secured, and payments to vendors can be streamlined. Machine learning uses past data to determine future trends that are relevant to a business.

GRC personnel such as risk managers, auditors, and compliance officials can obtain real-time information regarding the current state of affairs within each field.

With the right infrastructure, a company can automate GRC workflows and significantly cut costs. Using AI for compliance reduces the risk of mistakes made in record keeping, risk assessment, and data storage/analysis. Accuracy is key to compliance, and AI provides the tools necessary to achieve this goal by ensuring large data stores don’t become a liability for the company.