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.

 

In-Memory Analytics: The Latest BI Innovation

Business intelligence is a rapidly-evolving discipline.  And, as companies strive to maximize the value of their enterprise information assets, new technologies and techniques continue to emerge at a rapid pace.  The latest BI methodology to spark interest among users and industry experts alike is in-memory analytics.

The primary goal of in-memory analytics is to eliminate standard disk-based BI deployments, which are typically relational or OLAP-based.  These traditional implementations come with numerous drawbacks such as poor flexibility, limited scope of analysis, and slow response times.  With in-memory analytics, the reporting software performs all needed analytical functions at runtime – including data retrieval and storage, manipulation, calculation, formatting, etc. – within the memory of a 64-bit server.

Why are so many vendors and their clients suddenly embracing in-memory analytics? And what are the benefits to this approach?

The key advantage of in-memory analytics is speed.  Because queries and related data reside in the server’s memory, report generation does not require any network access or disk I/O.  This will dramatically increase the performance and reliability of the data warehouses and back-end databases in which the required report data exists – particularly when the report in question has a large answer set.  Therefore, regardless of the size and complexity of the query, or the amount of information it will return, users who leverage in-memory analytics will get faster answers, without any negative impact to operational systems.

The second key benefit is affordability.  In the past, memory costs were prohibitive, and 32-bit architectures offered limited processing power and storage.  But today, the costs associated with memory continue to decline, while 64-bit computing delivers much greater memory stores.  This makes in-memory analytics a less expensive and more feasible way to operate an enterprise business intelligence environment.

In-memory analytics can also dramatically reduce dependence on IT personnel, because it reduces the data management burden for reporting and analysis purposes.  For example, it can potentially eliminate the need to build, deploy, and maintain OLAP cubes. And, it can cut down drastically on data warehouse maintenance.

The benefits of in-memory analytics are clear, and experts agree that more and more companies will utilize this technique in the coming years.  In fact, Gartner anticipates that by 2012, 70 percent of Global 1000 companies will load detailed data into memory as the primary means of improving the performance of BI systems.