In this Information Map, there are some main parts to focus on that you will probably find in a lot of companies from different sectors:

• eCommerce (Online shop/portal): for online selling data.
• CRM (Customer Relationship Management): for data related to any interaction with the customer out of the selling data (registered in the company ERP)
• ERP (Enterprise Resources Planning): for all the company transactional data (orders, invoices, warehouse movements, etc.), you get info from the ERP launching queries to it. The ERP will answer about the register in its databases that accomplish the conditions established in the query. This kind of data exploitation takes a very long time when you work with massive data.
• DW (Data Warehouse): it contains the same ERP data but is organized in an easy-to-use way, and with periodic data consolidation that avoids the query way that implies losing a big amount of time to get results.
• BI Analytics (MS Power BI, etc.): it means organizing the Data Analysis into dashboards with KPIs that allow a precise view of your business goals.
• Collaborative Tools (MS Sharepoint, MS Teams, Slack, etc.): this part of the system has the data and the tools to collaborate on projects, processes and activities. These tools use to combine data from communication (video calls, emails, messages and chats), project management (Kanban), scheduling (calendar), people organization (teams management) and work/project-related file storage.

What is Big Data

Big Data is a massive amount of data that could be managed in order to improve some business process results. As stated before, this data came from very different sources and in very different forms.

Image by Olga Hmelevskaya on Freepik

Big data is the ability to manage an immense volume of disparate data, at the right speed and within the right timeframe to enable real-time analysis and reaction. Big data is broken down into three characteristics (the 3Vs):

• Volume: How much data (more than a normal computer can process).
• Velocity: How fast the data is produced and processed (some cannot even be stored for processing).
• Variety: The various types of data.
• The fourth V is Veracity: the triangulation of information that is possible to perform.

The main benefits of Big Data for a business are:

• Better customer insight
• Improved operations
• More insightful market experience
• Agile Supply Chain Management
• Data-driven innovation
• Smarter recommendations and targeting.

The real breakthrough in Big Data came when companies like Yahoo!, Google and Facebook realised that they needed help monetising the huge amounts of data that their offerings were creating.

These start-ups needed to find new technologies that would allow them to store, access and analyse vast amounts of data in near real-time so that they could monetise the benefits of owning this amount of data about the participants in their networks. Their resulting solutions are transforming the data management market. In particular, the MapReduce, Hadoop and Big Table innovations proved to be the sparks that led to a new generation of data management. These technologies address one of the most fundamental problems: the ability to process large amounts of data in an efficient, cost-effective and timely manner.

The New Data Landscape: Bridging Big Data and Artificial Intelligence.

We have seen in the previous section that an Information System consists of the interaction of several elements, remarking between them: the data, the computer system and the user. In this interaction, human users establish how to get and process data, analyse the process output, make decisions, and very often introduce those decisions in the form of new input data to be processed or communicated. In this process, the user analyses the results and takes action continuing the process flow with their analysis result, but not only this. The human system user learns during the analysis process and changes how the user analyses the data; in fact, the user changes the algorithm used in the analysis and, sometimes, how the data set is organised as well.

This data knowledge updating process (over algorithm and data set) is based on experience and judgement about these experiences. Experience depends on how many times you realise the process and how big the amount of data you process is. The bigger the data, the bigger the frequency of processing, and the bigger the learning is.

This learning potential has been known for a long time ago. The term “artificial intelligence” was coined as long ago as 1956. In 1950, Alan Turin stated: “Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child´s? If this were then subjected to an appropriate course of education one would obtain the adult brain.”

Source: Ted Talks

And here comes the machines, the big data and the processing technologies. In the last decade, it produced the confluence of:

• The vast amount of available data (coming from social media interaction, online shopping, transactional data from banks and physical stores, sensors, etc.) is called Big Data.
• The development of new processing methodologies that allow getting actionable information results without the need to invest in expensive processing central units (parallel computing and cloud computing).
• The development of the sensors sector allows exponential growth of the Internet of Things (IoT). IoT lets your freezer know if you need to purchase something better and earlier than yourself or allows your house to make efficient use of energy, enlightening or warming the areas of the home that you are using or going to use soon.
• AI methodologies, depending on data and work complexity, could be applied looking for:Automation. Is the application of AI to replace human decision in a repetitive process. There is efficiency improvement derived from its application but no learning.Augmentation. Is the application of AI to improve the scope or the efficacy of the process derived to the algorithm training exposing it to new and bigger datasets and extracting learning conclusions from the right or wrong decisions offered by the algorithm.

Source: Accenture

Artificial Intelligence Methodologies and Tools Used over business processes

AI Methodologies

Machine Learning means the ability of the AI-based tool to learn. ML is defined by Computer Scientist and machine learning pioneer Tom M. Mitchell: “Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience.” ML is one of the ways we expect to achieve AI. Machine learning relies on working with small to large datasets by examining and comparing the data to find common patterns and explore nuances.

The function of a machine learning system can be:

• descriptive, meaning that the system uses the data to explain what happened (e.g. Social media usage and engagement data such as Instagram or Facebook likes);
• predictive, meaning the system uses the data to predict what will happen (e.g. managing performance expectations and avoiding risks); or
• prescriptive, meaning the system will use the data to make suggestions about what action to take (e.g. investment decisions).

Deep learning is about computers learning to think using structures modelled on the human brain. In fact, all deep learning is machine learning, but not all machine learning is deep learning.

The Deep Learning approach uses logical structures that are more similar to the organisation of the nervous system of mammals, with layers of processing units (artificial neurons) that specialise in detecting certain characteristics of perceived objects. Deep Learning computational models mimic these architectural features of the nervous system, allowing networks of processing units within the overall system to specialise in detecting certain hidden features in the data.

When our brain's predictions do not accurately predict the future, we become aware of the anomaly and this information is fed back to the brain, which updates its algorithm, allowing it to learn and improve the model.

Source: edureka.co

AI Impact on General Business Processes

Depending on the economic activity sector and its position in the sector value chain, each organisation has a different Process Map.

A Process Map is the structured set of processes that a Company uses to create value. Examples of processes are R&D, Marketing, Financials, Manufacturing, etc.

In each one of those processes, AI could create an improvement. Here we show you some of that processes with some AI standards solutions available on the market.

Product Portfolio Development

Cover all customer needs and trends with the best product range, achieve industrial protections and promote diversification of risks in product categories, channels and customers.

Market surveillance

Business intelligence through systematic identification of trends, consumer insights, anticipation of new regulations and alert systems for the early detection of threats and opportunities.

Opportunity management

Support for the introduction into new markets, new categories, new clients and assessment on large account customers.