The transformative power of Business Intelligence 4.0
Technological advances usually occur in waves. They can be huge or broad. In certain instances, however it happens incrementally. In these instances, companies typically don’t know that a change is coming their way until technologically advanced competitors push them into a disadvantage. The next phase of technology that will improve business intelligence is already on the way and thorough research offers an excellent opportunity to comprehend and implement the next wave with Business Intelligence (BI) 4.0.
Business intelligence 4.0 is built on the decades of data analytic and business intelligence technology and it is the most comprehensive range of decision support tools that we have seen to date. At first there was BI 1.0 which was primarily comprised of the on-premise database and computing resources which were used to produce reports based on case-by case management requirements. In the next generation, platforms moved to the cloud and were able to deal with massive datasets, often referred to as big data. The 3.0 generation introduced real-time decision dashboards as well as mobile integrations that allowed enterprises to make use of data on their own (so-called self-service BI). It is true that in many aspects, this expansion continues to grow — but with exciting new possibilities that create new opportunities in the era of BI 4.0.
The New Generation
The previous generation of business intelligence attempted to give actionable advice to business leaders however, business intelligence 4.0 was the first one to really achieve this. Modern tools are equipped with this ability, utilizing the latest cognitive and predictive analytics aided by the latest generation of machine learning.
The latest cognitive intelligence technology can continuously analyze data streams from many sources and are that are powered through the Internet of Things (IoT). This allows tools for decision support to process and ingest massive amounts of data, generating real-time information. The technology, such as graph databases as well as the latest advancements in machine learning make use of this massive processing capacity to produce decision-making suggestions, both strategic and tactical both in the short and long-term.
Utilizing BI 4.0 tools to be an executive is absolutely transformational. Through anticipating future issues as well as recommending solutions and using data to think like humans would be able to, these tools directly create advantages in the market. These benefits naturally encourage massive adoption of these revolutionary technology and facilitate the efficient execution of people’s daily activitiesand so much so that companies who aren’t yet taking advantage of the advantages of BI 4.0 are consistently outperforming their competitors. When they equip their teams with the robust set of business intelligence 4.0 tools, executives are able to successfully transition into the digital era with a renewed emphasis on making decisions based on data.
INDUSTRIES 4.0 and ANALYTICS:
Processing and storage of data
One of the main reasons for an important aspect of the Industry 4.0 boom is the ability to analyze data in real-time, enabling instantaneous decision-making ( edge data integration). The ability to differentiate between normal and abnormal behavior can result in savings in terms of cost and time that should be considered the new analytical situation has altered the method of working on the operational scale. Analysts too this implies a shift moving from working with databases to working with data streams. streams of information, from using information generated by business processes to data from the environment which are heavily influenced by hardware.
However, we’ren’t just speaking about edgeanalytics however, we’re also talking about predictive analysis. The capability to implement models directly on the machine or on the hardware connected to the sensor will allow complete control over the future performance of the production line, stopping quality variations, avoidance of unplanned stoppages because of mechanical problems or allowing scheduling of automatic maintenance actions.
In this way, we have to be mindful of the legal limitations (for example GDPR) and restrictions on communication that demand us to permit that data is stored when there are connection issues and ensuring that we have a sufficient bandwidth that allows for a smooth streaming of data.
When data have been received and stored, it is time to use the data we’ve accumulated. There are a variety of options available and the ability of analytic representation of data is virtually limitless – from dashboards to the monitoring of alerts in real time and management using decision-making tools to the display of data from the past and the evolution of a sensor or alert indicators.
This is when one of the most significant obstacles usually arises in these type of project, as it is essential to understand in detail the capabilities of representation on every platform in addition to their ability to process, alter and display data at the appropriate speed. With the many available alternatives, it’s essential to understand the technical limitations of every platform, their advantages they provide, as well as the inherent constraints of your project (on-premise or cloud-based solutions and refresh rate, etc. ).
When working on every Industry 4.0 project, one of the main factors is the ability to predict analysis that can be incorporated through the use of models at the edge layer as mentioned previously expert systems, expert systems, fundamental algorithms, or even machine learning. Based on the needs of the project it is possible to require the use of a simple temporal projection an analysis of trends or in extreme situations applying a deep learning or neural network algorithms. This is where the expertise of various platforms and expertise in project management come to the fore.
We must be aware that the information required to build the model will be accessible following the installation for the sensors system. An accurate programming of the different phases of the project is required, so that we can allow for the collection for a testing set data, and to verify them as per the model is required to create.
The ability of the data scientist to capture business requirements in solid predictive models, with the highest degree of accuracy is essential. Current trends are to give the data scientist a dual job: on one hand, the data scientist is person who abstracts the business requirements (citizen Data Scientist) and in the second the technical profile is capable of determining the most suitable algorithm for every need and then parameterize it to the best method.
There are many solutions that allow the creation for Industry 4.0 projects, regardless of what the type of project. Today, the trend is to offer analytics tools that are cloud-based including SAP Analytics Cloud, Microsoft Power BI and Qlik Sense Cloud. These platforms come with very low cost, don’t require any infrastructure, and are extremely flexible with regards to licensing.
Furthermore they also provide multi-platform experience (web browser and mobile phones, tablets) and have a contemporary and pleasing look and feel, and are extremely user-friendly.
However it is equally important to consider the flexibility of cloud-based services (Amazon Web Services, Microsoft Azure, Google Cloud) which permit to be adapted to any scenario by activating the required modules and only paying for the features we utilize. Furthermore, we should think about specific tools to help us predict maintenance, like SAP Predictive Maintenance and Service or SAS Asset Performance Analytics.
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