Business Intelligence Advantages

When Artificial Intelligence Meets Data Analytics

If you are from an organization that strives to function in a highly-technological environment, then it is crucial that you know the relation of big data and artificial intelligence: the latter depends heavily on the former for success, while also helping organizations unlock the potential in their data stores in ways that were previously cumbersome or impossible. Leveraging well-managed and presented data can improve organizations big-time. The problem is, handling data is stressful due to a variety of reasons.

Data Analytics is the process of making sense of and transforming data into useful knowledge. This process is composed of many stages and phases, and while there are software or tools that exist to assist, data-wrangling - the exhaustive process of cleaning and organizing data - is still rarely addressed. Obviously, practical data analytics is painful, and a helping hand in the form of automation through artificial intelligence can make a huge difference in this field. 

To revolutionize the speed and efficiency with which data can be transformed into useful knowledge is the goal of The Alan Turing Institute’s Artificial Intelligence for Data Analytics project, otherwise known as AIDA. According to the initiative, it aims to combine multidisciplinary work from machine learning, semantic technologies, and programming languages to: (1) Build AI assistants for individual tasks, (2) Build an open-source platform and integrate the assistants into the platform; and (3) Provide exemplar use cases of real-world data wrangling. It also aims to solve some data engineering challenges such as. (a) data organisation (data parsing, integration, dictionary, and transformation); (b) data quality (canonicalisation, missing data, anomaly detection); and (c) feature engineering.

Data analytics required a lot of effort but with the help of AI, not only did it speed up the process but also allowed depth in making sense of data in the past.

AI-related initiatives like AIDA fuel better opportunities in insights and knowledge production since it is creating new methods in analyzing data, and data analytics has become less labor-intensive. Data analytics required a lot of effort but with the help of AI, not only did it speed up the process but also allowed depth in making sense of data in the past. In fact, AI is now deemed promising as it thrives in different kinds of industries. 

AI in Action

AI and machine learning are powerful levers when it comes to big data. Together with the power of human intuition, they are critical to helping businesses have a more holistic view of all of that data. It revolutionizes the way you get rules, decisions, and predictions done which entail the increase of the potential to dramatically improve the productivity of data scientists, analysts, and researchers benefiting governments and organizations because it will allow faster delivery of insights and decision-making. 

• Insurance Sector 

A recent study from the Organisation for Economic Co-operation and Development (OECD) (2020) encourages the insurance sector to prepare incorporating AI in their specific context. For instance, having more data leads to improved predictive analytics, enabling pricing that is better suited to expected risk. And since insurance is based on predicting how risk is realised, having access to big data has the potential to transform the entire insurance production process. 

• Healthcare

Payers and providers of care, and life sciences companies have started employing several types of AI in various categories such as diagnosis and treatment, patient engagement, recommendations, and administrative practices (Future Healthc, 2019). It will take many years before AI completely erases humans in medical domains, but at the moment, it has made a promising impact in the medical field: 1) Algorithms are already outperforming radiologists at spotting malignant tumours, and guiding researchers in how to construct cohorts for costly clinical trials; 2) Machine Learning is deemed to have the primary capability behind development of precision medicine; and 3) AI-based capabilities are deemed effective in personalising and contextualising care by, for example, sending messaging alerts with relevant and targeted content that provoke actions at moments.

• Government

With AI in data analytics, data-driven governments are reaping a more efficient and convenient delivery of public services, and better-informed policymaking with predictive analytics, policy simulations, and real-time early warning systems because the use of technologies allows them to observe their citizens and physical environment with unprecedented data density and analyse these observations (European Liberal Forum, 2019).

While AI is yet to be explored, it has been actively changing and making a big difference not just in the field of data analytics, but also in the market as a whole.

By bringing the fusion of AI and Data Analytics, Croyten can assist you to ensure that your organization can potentially reap the benefits this advancement is opening. While AI is yet to be explored, it has been actively changing and making a big difference not just in the field of data analytics, but also in the market as a whole. Thanks to Artificial Intelligence, new products are developed which are better than before, and the opportunity of autonomy it offers saves businesses huge amounts of time, leading to quicker decisions gleaned from data. 

Data is the new oil, they say. If so, data analytics is the vehicle that processes this oil, and artificial intelligence plays the role of an upgraded machine system. Combine them altogether and they can make your organization stand out from the rest.

A Collections of Data and Things

 

Within seconds, a company executive in the U.S. can know exactly how many parts their global manufacturing plants are producing. A delivery company can tell you exactly to the minute when their truck will be arriving. A utility company can monitor usage across the country and know when it’s reaching a peak. All of this can be done because of the Internet of Things (IoT) and Big Data.

The IoT is basically a collection of Internet - enabled devices or sensors, other than your computer, which are connected to the Internet and can send and receive data. Big data is what you get when all of this information is collected and analyzed.

Devices such as smartphones, scanners, sensors, and GPS can gather and distribute a lot of information. IoT technology allows the input from these devices to be pulled together.
Once it's all been collected, companies can utilize big data analytics tools to improve business operations, manage equipment and people, target marketing and make their business run more effectively and efficiently.

The IoT is forcing people and companies to change the way they look at things. Information is being funneled fast, in large amounts, structured and unstructured and from places we never thought we’d get information from. Refrigerators talking to smartphones for a shopping list, or fitness trackers measuring your burned calories, sensors sending vital health data to doctors to monitor their patient‘s health in real time, and anything else you can imagine. Vendors can then use that information for marketing directly to consumers or provide better and timely service. Inventory in stores could soon be reliant on just a sensor on a shelf that indicates when an item needs to be restocked.

The next step for businesses is to figure out how to make the most of the data pouring in from things like smart meters, devices, and sensors. How is this data going to affect your next business decision and how is it all going to be analyzed?

Companies need to plan for a continuing influx of data as more devices become connected and interconnected. You need the bandwidth to store data, the real-time analytical tools to analyze it, and the ability to monetize it and turn it into something profitable. Without a plan, you could be left behind.

 

Big Data, Big Decisions

Big data. It’s a pretty broad term, but it’s used to describe data sets that are so big or complex that in order to get the most value out of them companies need to use enhanced data applications; and more importantly know how to manage all of the information.

When it comes to big data, it’s not so much about how much you have, but more about what you can do with it. Managing this data means creating a structure that can store, process, and organize large volumes of structured and unstructured data.

For example, a typical bank offers its customers multiple products; a mortgage, car loan, checking account, saving account, credit line, etc.  In today’s economy customers also conduct transactions online and via mobile devices, and provide feedback on services via social media. Should all that data be stored in different places? No, but banks and other firms are starting to recognize that having all that relevant data stored in one place can provide a wealth of insights about their customers, which is critical to better serving their customers and offering them customized products that makes sense. Having that data in one place will enable efficient data management control and a single-client view.

Regardless of the size of your company or the data being brought in, big data can provide a whole new way of approaching big decisions.

A consumer-oriented company can use big data to listen to, learn from, and leverage consumer feedback to produce targeted B-to-C campaigns. All of the feedback collected from social media and surveys allows a company to build and update consumer profiles and then execute personalized marketing and advertising campaigns.

Insight-driven organizations (IDO) need data to drive decisions. A lot of the time, though, there is so much data these organizations don’t even know where to start. It can be a very long journey to take big data and turn it into insight-driven material. As an IDO you need to figure out what insights will be most impactful with your clients. Then you need to know if you have the right data and analytics to create these insights, and if not, how do you go about getting that information? Once those insights are available how do you create a strategy to implement them in day-to-day decisions making?

Regardless of who is using big data or how it’s being used, there are always concerns about security. Most companies don’t have the infrastructure to store big data on their own IT networks, which means they are either going to be using the Cloud or a third party storage. Transferring data out doesn’t mean to companies transfer their liability. With data coming in from all avenues; social media, emails, files, etc., there are more entry points that need to be protected as along with external access points where the data is being housed.

Finding the most accurate and secure way to use big data will lead to better decision making - which will result in more efficient operations, reducing costs, and reducing risks.

Tailoring Your KPIs to Your Business

In recent years, key performance indicators (KPIs) have become critical to accurately gauging the state of a business and effectively measuring operational performance.  Business intelligence tools have provided companies with a great way to measure these vital metrics to ensure that their most important objectives and goals are being met.

Yet, few companies truly understand what KPIs are all about.  True, many BI packages on the market today come complete with pre-defined reports, charts, and graphs for KPI monitoring.  These are usually based on standardized measures that apply to most all businesses in every industry, and are designed to accelerate the development and implementation of comprehensive performance management strategies across an enterprise.

But, when it comes to KPIs, the “one size fits all” approach is not always best.  While standardized KPIs are a good start, they alone are not enough.  Every company is unique in its purpose and mission.  Therefore, each one requires different metrics and performance indicators to determine how well it is achieving its most critical goals.

In addition to standardized performance metrics, companies should create a set of custom-tailored measures that follow the guidelines below:

• Base them on key business drivers.  What is your business really trying to achieve?  Are you trying to boost market share?  Or is profitability and shareholder equity your primary goal?  Perhaps customer retention is most important to you?  Define three to five key business drivers, then identify the business processes that support them.  It is the effectiveness and impact of these activities that your KPIs should be tracking and assessing.
• Make them department-oriented.  What is relevant to one department may not be so crucial to the next.  For example, your sales department will have different goals to achieve then your accounting or customer service teams.  Therefore, each division should have their own set of metrics – that tie directly into the overriding KPIs for the enterprise – to help them determine if they are meeting their plans, and what corrective action, if any, needs to be taken. 
• Set role-based goals. Even within the same department, goals will vary from one employee to the next.  That’s why its important to break larger objectives down into smaller measures, so each worker will know what is expected of them, how well they are doing, and how their role impacts the performance of the department.

In the long run, a performance management strategy won’t work if the KPIs being measured aren’t the right ones.  Be sure to consider company mission, structure, and other unique factors and traits when outlining your plan and defining your performance metrics.