HomeBUSINESSReal-Time Analytics: What It Is& What Are The Advantages For Companies

Real-Time Analytics: What It Is& What Are The Advantages For Companies

In a crucial phase of the post-pandemic recovery – a very rapid and bursting recovery in specific sectors -, companies urgently need to analyze the scenario in which they operate and to identify, based on the data, in particular quality data, on which businesses o product portfolio aiming, without wasting time, concentrating investments where they are needed. 

Real-time analytics is, therefore, crucial because it prevents businesses the risk of wasting precious resources in a thousand streams, facilitating the achievement of targets in real-time, reducing the time-to-market and gaining greater effectiveness from the actions carried out. These are its main benefits, in fact, together with its ability to increase productivity.

Companies must therefore exploit Big Data, integrating these resources into all business processes. Only real-time data analysis can offer managers, who must act promptly, a precise and timely snapshot of current trends, seizing the best opportunities as quickly as possible.

What Is A Real-Time Examination? 

An ongoing investigation is a discipline that applies rationale and math to information to settle on better choices immediately because of experiences. Real articulation time implies that sometimes the inquiry ought to be finished very quickly or minutes before the appearance of new information to examine. An ongoing examination is separated into two sorts of investigation: on-request and constant. 

On-request ongoing investigation: infers the solicitation by clients or frameworks of a question that gives the consequences of the examination completed progressively; Regular, continuous examination: it is more proactive and cautions clients or triggers reactions as occasions happen. Information aces are associations that can proactively make, cycle and take advantage of information to accomplish their corporate mission, performing business destinations and creating advancement.

What Big Data Means

Big data is based on large amounts of data, which take up a lot of storage space, in the order of terabytes. The data represent, in fact, the priorities that the heads of companies find themselves having to take into consideration. Dating is “that set of techniques that allow the conversion into digital format – that is, into data – of anything” (films, books, voice messages, body movements, etc.). If words, geo-localization, social interactions and things connected through IoT are transformed into data, at any moment, they disseminate traces on the network (Online footprint).

Vast amounts of data (in terms of volume), characterized by different formats (in terms of variety), stored and processed at an ever-increasing pace (in terms of speed) (often in real-time) represent Big Data, which constitutes the productive factor in a data-driven economy, analyzed through Data mining or analysis techniques that allow the creation of new services, improve existing ones, innovate production and distribution processes, decline the offer of all products and services (even if not digital), increasingly in line with the needs of consumers and citizens.

Furthermore, Big data is defined as high volume, high speed and an excellent variety of information assets capable of being processed to offer insight to decision-makers to favour automation, time and cost-cutting and productivity increase.

Also Read: Artificial Intelligence: How It Will Transform Our Lives

AI-Powered Cloud Ecosystems

In recent years they have reached a reasonable degree of maturity. Intelligent tools with augmented forecasts and decision-making are available. The challenge is to make sure your business is ready to use it.

Edge Analytics

With the spread of cloud computing, platforms are increasingly able to integrate edge applications into their ecosystems. This implies that large volumes and varieties of data can be stored and analyzed locally without being sent back and forth between the user and the cloud, significantly improving efficiency. In some cases, this helps mitigate security and privacy risks. Since the main barriers to using advanced analytics are organizational and non-technological, companies must choose data analytics platforms according to their needs.

Microsoft Power BI

It is an end-to-end analytics solution that enjoys the advantage of having an interface of immediate familiarity, thanks to the integration into the Office 365 ecosystem. It has standard or enterprise licenses. Power BI has passed the threshold of the first decade and represents the analytics of choice for hundreds of thousands of companies. Still, in the latest versions, it boasts constantly evolving automation and augmentation capabilities.

Oracle Analytics Cloud

The king of databases has updated its product range in light of cloud and AI capabilities. The most sophisticated aspect of this platform lies in the use of its natural language. It manages queries in 28 languages, more than any other competitor.

Thought Spot

This is another complete platform. The suite allows for natural language query datasets and emphasizes a user-friendly, pick-up-and-play approach to analytics. It incorporates UI features familiar to anyone who uses social media: self-curated feeds, real-time insights, etc. The formula is winning in terms of the engagement generated by users of social networks. The AI-powered assistant, SpotIQ, uses machine learning in a predictive perspective and provides ad hoc suggestions, offering neglected insights or suggesting alternative methods.

Apache Spark

It is an open-source that has gained popularity over the past six years. The ecosystem is rich in extensions and plug-ins, which make it dedicated to the enterprise segment: in particular, the MLib machine learning library stands out. Thanks to its community of users and vendors, it offers support and assistance. At the same time, the applications adapt to the skill levels of the employees—strong point: integration with Apache projects such as Hadoop.

What Are The Goals Of Real-Time Data Analysis?

The pandemic highlighted the need for companies to reinvent or accelerate the data-driven strategy. They need the skills to meet the expectations. Today only 1% of companies have a Chief Data & Analytics Officer (Data analyst or Data scientist) responsible for data governance and data science. Still, the profiles of the Data visualization expert and the Data engineer are also prevalent, while the data science manager grows slightly.

Thanks to the use of professionals in the sector, companies can achieve the objectives of Real-time Analytics:

  1. Improve their data and the ability to enhance them
  2. Insert new skills (search for professional profiles with reporting and visualization skills)
  3. Focus on the quality of real-time data
  4. Technological investments to integrate real-time data from different sources
  5. Optimize project management skills in advanced analytics
  6. Invest in data visualization software
  7. Intensify the work of data science
  8. Accelerate data-driven cultural change

What Are The Advantages Of Real-Time Analytics For Companies?

Extensive data analysis brings significant benefits to businesses in two areas. The first is linked to the need for more excellent knowledge and understanding of customer behaviour (in the B2C environment), to be achieved through the analysis of structured and unstructured data, coming from various sources, both direct from the company and online, through the traditional research in synergy with semantic analysis, and through the connection of purchasing models and behaviours aimed at creating greater customer loyalty, as well as the possibility of cross-selling activities, always deriving from more complete and in-depth profiling of the customer.

The second area concerns the optimization of internal processes and, therefore, the increase of business productivity, a decisive factor for the industrial and IT world, thanks to the reduction of time-to-market and the greater effectiveness of the actions carried out:

  1. Understanding of customer behaviour
  2. Process optimization
  3. Identify the link between the behavioural and the purchasing model
  4. Increase in business productivity
  5. Customer loyalty
  6. Up-selling and cross-selling activities on existing customers
  7. Risk reduction
  8. Exploration of new market opportunities
  9. Analysis: predictive – product portfolio – Revenue forecasting
  10. Operating expenses monitoring
  11. Customer satisfaction analysis
  12. Cluster construction of customers
  13. Fraud detection and prevention
  14. Regulatory compliance
  15. Capital expenditure optimization.

Also Read: The IoT Is Helping Digital Twins On Their Way

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