It is often said that data is the most valuable asset a business can have; the oil of a digital era. But data itself, while interesting, often leaves out a variety of important details – creating a need for analytics. And how we complete these analytics has evolved – and will continue to do so; particularly with the rapid proliferation of AI tools and technologies.

Some benefits associated with AI and analytics include:

  • Increased productivity via instant access to the right data through better classification processes
  • Improved speed, accuracy, and efficiency throughout analytics and data management practices
  • Facilitating clear compliance with legal and similar requirements
  • Cost saving by finding the best ways to achieve a wide range of tasks

To better understand data, analytics, AI, and how they all go together, we will answer the following questions:

How is AI making analytics more powerful?

How is AI making analytics easier to produce?

How is AI making analytics more accessible (easier to consume)?

AI Enables More Powerful Analytics  

I started my career as a management consultant. Excel was our temple for analytics. For a recent graduate, macros and connected models perform miracles but albeit at great effort. However, our reach was extremely limited compared to the possibilities of today. Anything with images, text, audio or video was unprocessable by Excel in any intuitive way.

Advances in AI greatly expand the scope of analytics when compared to the days when excel was the primary analytics tool.Some ways that AI is becoming integrated in analytics includes these areas:

  • Natural language processing (NLP) : Understanding, interpreting, and generating human language.
  • Computer vision: The ability for computers to be able to see, identify, and process images in the same manner as humans.
  • Machine learning: The use of statistical techniques to enable computers to ‘learn’ with the data given, rather than being programmed explicitly for a certain function.

These techniques are opening up the ways that data can be used – and how to achieve this usage. They do so by teaching a machine to understand, communicate, and interact in a way that we consider to be ‘human’. Having this capacity is preferable to an actual human because machines can continuously receive and analyze inputs and use them intelligently – without the same biases and weaknesses associated with human analysis.

Some analytics techniques that can be enhanced with AI and machine learning include:

Prediction: Using short and long term variability in data to enhance forecasting efforts.

Pattern recognition: Understanding normal trends in order to spot anomalies, as is often the case in fraud detection.

Classification algorithms: Grouping and organizing of data, includes clustering.

Regression algorithms: Estimating outcomes when problems have an infinite number of solutions.

Image recognition: Recognizing subtleties within a certain image, also includes entity recognition tools.

Achieving Predictive Analytics

Analytics have gone through a huge transformation from the earliest days of descriptive analytics. AI has supported the evolution through four key types of analytics:

  • Descriptive Analytics (What happened?): Showing what is actually happening based on given data; often usually via dashboards and reporting tools.
  • Diagnostic Analytics (Why did it happen?): Analyzing past performance to determine not only what happened, but why it happened.
  • Predictive Analytics (What could happen?): Describing what scenarios are likely to occur, often in a predictive forecast.
  • Prescriptive Analytics (What should we do?): Making suggestions about what should be done and their basis.

AI has supported the transition to prescriptive analytics because it is taking the outputs from machine learning algorithms and deep learning in order to better predict future events.

Image source: Gartner

AI, Analytics, and Manufacturing 

Analytics are one of the most important parts of running a successful manufacturing business. To increase profits, it is necessary to find new and better ways to eliminate inefficiencies and drive revenue – via analytics. Manufacturing analytics can be supported by AI in the following ways:

  • Supporting enhanced predictive maintenance programs via machine learning to detect anomalies. For example, the analysis of time series data from IoT sensors (such as those for vibration or temperature) to build forecasts for useful life of a component.
  • Using machine learning in conjunction with other AI technologies for better asset tracking, supply chain visibility, and inventory management. This can also reduce supply chain forecasting errors through better understanding of transport and warehousing costs.
  • Optimizing operations on the manufacturing floor through real-time monitoring and analytics supported by the 24/7 availability of AI technologies.

These are just a few of the ways industrial and manufacturing analytics can be supported by AI technologies. Over time these factors will only stand to improve and evolve to become even more effective in their tasks.

Tools for More Powerful Analytics

Some tools that can enable more powerful analytics include:

NameFoundedStatusNumber of Employees
Amazon Machine Learning1994Public10,001+
BigML2011Private11-50 H2O2012Private51-200
IBM Watson1911Public10,001+
Microsoft Azure Machine Learning1975Public10,001+
SAS Analytics Suite1976Public10,001+

AI for Easier Analytics Production

Making analytics easier to produce is a key goal of many major firms, especially given the current shortage of skilled data professionals.  AI can also help with several common challenges related to data management, which ultimately lays the foundation for better analytics. These challenges, and some common AI solutions to them, include:

  • Data accessibility: Finding the right balance between speed and price.
    • AI solution: Using ‘smart’ storage engines that through machine learning are able to determine which data is rarely used and can be stored accordingly. This can then become automated so that data is always organized into the right place for its usage need.
  • Data retention: Compliance factors encourage decision makers to retain tons of data, plus reluctance to plan and execute a formal retention program.
    • AI solution: Similar to accessibility concerns, machine learning can tell organizations what data is never used and streamline it for any later ‘human’ analysis necessary.
  • Data integration: How to combine sets of data into repositories for new business queries.
    • AI solution: When developers are building integration methods to supply their data repositories with, they have to create different integration methods to successfully access different sources. Machine learning simplifies this process with ‘mappings’ between sources and repositories.
  • Dark data: What to do with incoming structured and unstructured data too great in volume to properly manage.
    • AI solution: Machine learning developed algorithms can help to sort data that can then be automated.

Challenges to Data Analytics 

In addition to achieving higher quality and more accessible data to be used in analysis, AI has also aided in overcoming a number of common challenges associated with analysis.

1. Security and privacy: Tools for analysis, storage, management, analysis, and utilize multiple different data sources and formats. This ultimately leads to a greater risk of data exposure, sometimes making personal data particularly vulnerable. Subsequently, as data sources (and volume) increases, security and privacy concerns also do. This makes it essential for analysts and data scientists to consider these issues and deal with them in a manner that will not lead to the disruption of privacy.

AI solution: By automating complex processes, ML technologies are able to detect threats in earlier stages. This means that human intervention can be integrated faster, minimizing any damages that may occur.

2. Multiple data sources, often unstructured: There is an ever growing volume of data that is produced and needs analysis, especially when it includes the production sources of the data.

AI solution: With machine learning, organizations can add structure to data and automate related processes. Some examples of machine learning tasks to achieve this include:

  • Keyword extraction
  • Object and landmark detection
  • Topic and concept recognition
  • Handwriting analysis
  • Speaker recognition
  • Entity recognition

3. Lack of technical training or knowledge within a team: Particularly in the case of big data, there is a shortage of qualified data scientists and analysts available to fill open needs. This means that organizations can run into problems where analysis either is not carried out or is carried out incorrectly.

AI solution: AI in itself is the solution to knowledge gaps. It eliminates issues related to lack of knowledge, bias, and other human qualities. Different AI tools on the market today are getting easier and easier to use for those outside of data scientists, opening up advanced analytics to a much greater range of people.

Some tools that can help to make the analytics process a bit easier include:

NameFoundedStatusNumber of Employees
Alphine Data Chorus2011Private11-50
Apache PredictionIO1991Private11-50
DataRobot 2012Private201-500
Infosys nia1981Public10,001+

Improving Analytics Accessibility with AI

Analytics are only as useful as they accessible With AI, the ability for users to ask questions and be able to easily and intuitively find answers can be greatly enhanced. This is supported heavily through natural language (NL) query. Today, some technologies are even advanced enough to catch subtle nuances and intent.

Some examples of AI uses that can be applied to analytics include:

  • Speech to text: Transcribing voice messages to text for more rapid analysis over a greater number of criteria, such as is the case with sentiment detection.
  • Natural language interaction (NLI): Telling a software to build a report based on a desired criteria without having run reports manually.
  • Natural language generation (NLG): Obtaining summaries of all analyses performed on a document collection or repository.

These capabilities mean that users can quickly and more easily make business decisions without spending hours digging through analytics and reporting dashboards.

AI & BI Analytics

Business intelligence (BI) activities are becoming more essential faster than professionals are available to fill the demand. This, in conjunction with data volume, has created a need for easier ways for executives and non-technical users to get the most value from data.

Subsequently, sometimes the reporting, dashboards, and data, can become dizzyingly complex, leading to delays and decisions based upon assumptions. Some examples of how AI can help business users to improve their BI operations include:

  • Machine learning with software such as IBM Streams and DataTorrent helping businesses discover anomalies in order to take immediate action for fraud analysis or gain better insight into online buying behaviors
  • Machine learning and AI supports bots who bring data to existing workflows in a low-impact way within a BI environment. When dashboards aren’t enough AI can bring insights beyond what would be possible traditionally.
  • Avoiding overload in a ‘real time’ environment. Business and data move at a breakneck pace and AI can help by making BI data manageable, leading to better information availability for decision makers.

These different improvements supported by AI throughout data analytics demonstrates not only how far we’ve come, but also how far we still have to go. However, from these uses it is clear, AI will be the foundation upon which many major improvements will arise. Interested in seeing other ways AI is impacting our lives? Check out our blog for more posts. Need an AI vendor for your new project? Take a look at our database of over 3,000 AI companies.

Featured image source

Leave a Reply

Your email address will not be published. Required fields are marked *