#dataanalytics

Analytics as a service (AaaS)

For the last 10 years was happened a number of technology advances that have changed the way of companies’ relation both between themselves and with their customers. We were overseeing keynote jump to the modern smartphone, «power shift» in the social media world where Instagram overtopped Facebook, appeared new generate technologies that have fixed firmly in the business. And finally, cloud was appeared. Big Data, AI, ML and cloud gave an impulse to develop a new product – «analytics as a service» (AaaS).

Business is increasing in a rapid rate and together with it data is growing too. For processing of such data volume companies need to make essential investments in different resources (human resources, software and hardware). But AaaS can solve this problem.

Analytics as a service is rated as an analytic software service via Internet. By using this technology solution organizations receive an access to data analysis without development in-house technologies that allows to cut expenses.

The demand for analytics is not limited by one business area but outspreads for all: manufacturing, retail, healthcare etc. Considering individual requirements of each company AaaS-providers and their customers have endless possibilities. This solution could be easily included into resources planning, manufacturing execution system, cloud services and security police.

AaaS benefits for business:

  1. Business insights

The market changes rapidly, consumers’ expectation grows constantly, macroeconomic conditions change dynamically. Considering such terms, it’s not enough for business to go with the times but it has to be one step ahead. And it’s not possible to make without analytics. Independently of scope each company is in need of in analytics. «Analytics as a service» can help here paving the way from data to ideas and solutions. With the help of AaaS company’s management easily get predictive data on the bases of which develops more effective business strategy.

  1. Improved customer experience

Satisfied customer is a formula for business success. That’s why one of the main tasks for each company is to increase customer loyalty by improving customer experience. With the advent of online channels data collecting has increased that stimulated companies to provide multichannel service. AaaS helps to unify data from different sources and create the single source of the right data.

  1. Predictive statistic for everyone

AI analytics is an indispensable business tool. With AaaS organizations get access to it and arrange data-driven decisional process across all hierarchy. It helps to save some time and resources.

  1. Competitive analysis

Companies have an opportunity to collect data from several sources, track field trends and seasonal changes and use this information in business processes. Deep analysis allows to compare company’s rates with business rivals’ rates and develop a targeted strategy for better results achievement. With self-service function AaaS users can rapidly and easily analyze data and create necessary reports.

Market is growing invincible, many new companies are appearing there and a business struggle is also growing accordingly. For flying level companies need to use advanced technologies and run analytics in daily routine.

Big Data Analytics can improve customer experience

Currently the world is composed of data that every day is being produced by businesses as well as individuals. According to a tally, in 2020 each Internet user created 1,7 MB of information every second, Google processed more than 40 000 queries, about 3 million emails were sent by people around the world. And those rates continue to rise rapidly.

This amount of data processing requires advanced analytic solutions, specifically big data analytics.

What is this?

Big Data Analytics (BDA) – is the complex of advanced analytic methods that are focused on the work with big and different size data (from terabytes to zettabytes) to uncover valuable information from structured, semi-structured and unstructured data sets from different sources. By using this tool, it is possible to uncover hidden patterns, unexplored correlations, market trends and customer preferences. BDA comes with technologies and methods like predictive analytics, statistic analysis, text analysis, data visualization, machine learning, artificial neural networks, spatial analysis, Data Mining, pattern recognition, simulation etc.

World data amount stupefies but opens huge capacities for business. For instance, in working with customers and their behavior. By collecting and analyzing data for each client it becomes possible to provide an individualized offer upon request. As a result, a company becomes more competitive, customer experience becomes higher and revenue is growing.

Some examples how BDA can help to improve customer experience:

More analytics – more possibilities

Data and analytics are the main tools of the modern business in the digital world. Trend of additional data and analytic decisions searching is actual currently. Organizations demands are growing, they want to find one technology for satisfying most of their requirements. It was a reason for advanced analytics developing.

Despite visible benefits of modern technologies usage, some companies stay away and don’t understand how they can use them. It’s mistaken opinion that advanced technologies are applicable and useful only for major companies like Google, Microsoft, IBM etc. Another one barrier is investments required to technologies implementation into the enterprise. Advanced analytics is a decision of such questions.

What is advanced analytics?

Advanced analytics is the combination of technologies (machine learning and analytics) for automatization of whole data pipeline (from data processing to results generation). It could be compared with an umbrella that includes many disciplines and has high use. Such kind of analytics is used in all business areas for events forecasting. For example, advanced analytics in marketing is used for understanding customers’ preference and their behavior changes.

Gartner describes advanced analytics as autonomous or semi-autonomous data examination with the help of sophisticated techniques and tools, that promotes deeper understanding, more precise predicting and recommendations creating. It gives a possibility for companies to perform calculation like «what if», that are used to forecast trends, events and behaviors. Advanced analytics is comprised of such areas as artificial intelligence, predictive analytics, data mining, data visualization, semantic and graphic analysis, neural networks etc.

Advanced analytics advantages:

 Applications of advanced analytics in business

  1. Right data gathering

Data is the bases of digital era. In consequence of analyzing and coming up with data-driven answers business processes became easier. It gives an opportunity for management to make different decisions easier and more efficiently. But the main task is to identify and collect the right data. Advanced analytics give a possibility to correctly identify necessary qualities and make them operational for goal achievement.

  1. Creating business-model to optimize results

Business-model creating comes from capabilities definition. Here data mining technology is actual.  This tool allows to make a lot of tests that will help to identify submerged patterns. However, a result will depend on how efficiently executives can use received information. Advanced analytics is a perfect assistant in this task and creating of business-model according to the working system.

Main data trends 2021

After 2020 everyone has feelings that the world will not be the same. It was enough restless and astable year for the whole word. But at the same time, it became an acceleration of digital transformation processes. Business and people had to accustom and fit into a new reality. Less digitized companies have become more vulnerable during the pandemic compared to high-tech market players. However, it also became a motivation for such companies to run digital. They had to digitize their processes, upgrade business-models, provide access to data and advanced training for team. COVID-19 has also become a proof that the data plays a big role, and everyone can use it to inform or misinform.

Data science evolves and matures and consequently many organizations try to increase their digital stability and switch over to the data-driven model. Critical important tasks like self-driven car development, protein folding, and algorithmic trading programs have been conducted using data science methodologies and technologies. It’s just a little part of examples. Data science using is much wider, new and improved data science tools will appear in the coming years.

The main data trends and forecasts for 2021

1. Forecasts with the help of data analytics

One of the main 2021 trends will become real-time analytics. According to forecasts, the number of connected to Internet of Things devices will reach 24,1B by 2030. Organizations collect much more data than previously and try to transform it into analytic information that can help to solve business tasks. Real-time analytics transforming data into insights gives a possibility to respond to situation instantaneously.

2. Databases

For the last 40 years companies have histed their databases locally. However, in 2021 and the coming years there will be a trend of databases deploying or migration to the cloud. According to forecasts, cloud databases will grow up to 75% by 2022. It is a reason of different requirements appearing that most likely will come with developing on cloud-native databases, more closely incorporating analytical and machine learning capabilities.

3. Knowledge graph

As the amount of data still grows rapidly, it becomes increasingly difficult to analyze it. Knowledge graph can help in this by closing the gap between human and machine. In the minds of Gartner, it is one of the main data trends.

Knowledge graph has a form of a facts set (description of objects, conceptions and events) connected by typed links. It becomes possible to create a better context for data through linking and semantic metadata. It promotes easy analysis, integration, sharing and data aggregation.

4. Augmented analytics

We generate about 2500 petabytes of data every day and in 5 years this number will grow to 463 exabytes. Data increasing created serious problems in its processing. Augmented analytics can help to solve them. Using ML and AI methods big data transforms into massively smaller and analyzable one. According to the Gartner research augmented analytics will become a driver of BI in 2021.

5. Data Visualization

Data visualization became a perfect assistant in 2020 that helped to understand current situation easier. Creating, critical understanding and evaluation of data visualization will become a fundamental skill for everyone.

It’s time to start working with the data and getting benefits

Currently business is going through a digital transformation stage that requires management quick reflexes to technology trends and business process review. The main trends include artificial intelligence, virtual and augmented reality, cloud decisions, big data etc.

Data analytics without a rival is one of the main trends. Technologies development is the reason of data quantity increasing. 90% of the world’s data were created during the last few years, and investments in Big Data amounted to $180 billion. According to BARC research enterprises using Big Data have increased their profit by 8% and cut costs by 10%. Beyond that the following  benefits were noted:

Many companies have already crossed over to digital technologies and generate several GBs of customer data. Such players as Facebook, Amazon, Google are working with big data actively and gaining ground giving priority to the quality of customer service.

In addition, data analytics is a basis for other technologies. For example, artificial intelligence systems learn based on analytics.

Understanding of data analysis and methodology of determining their accuracy gives a possibility to make valid and effective solutions that will lead up business growth and progress. All business solutions must be supported by exact figures and facts that serve the purpose.

Despite the strong performance, there are companies that don’t understand yet how they can start transformation and getting benefits from investments in this sector.

The transformation into data-driven business is a long-term process, that demands investments and following steps realization:

Basic methods for effective analysis

Increasingly С-suit leadership has BI implementing in work process on his mind. It is fairly, since a possibility to get additional information and data appears with help of such tool. However, without proper analyzing and understanding received data has the form of numbers set. Key goal is to form correct conclusions generation on the basis of which will be made best performing decisions.

It’s worth to remark that there is no one right way to analyze data. Above all it’s necessary to understand which data type you need to work with, and which goals have to be achieved. Based on this, methods of analysis could be changed and each of them will be effective in some cases.

Nevertheless, there are some basis and more effective techniques and most of data analytics software include them. Let’s describe 5 main techniques.

Quantitative and qualitative data

One of the most important element when making a selection of technique is a data type understanding (it could be quantitative or qualitative).

In the first instance – it is accurately measured and counted information like sales volume, click number and desired actions, profit, costs and other rates that could be transformed to exact number.

In the latter case – it’s just subjective information that was received resulting from different interviews with employees, customers or independent people. It allows to evaluate more qualitative aspects. By this technique will be received less transparent data than by quantitative.

Quantitative data measuring

1. Regression analysis – is a perfect tool for predicted data and future tendency calculation. This technique allows to specify connection between dependent variable and independent variable. Regression analysis helps to identify and understand relation between different factors in consequence of which it’s possible to optimize internal processes.

2. T-testing – is indispensable tool for current hypothesis checking, or rather to compare current data with hypothesis and assumptions. Such technique gives a possibility to forecast various courses of actions influence over enterprise performance. Also, it helps to find correlation dependence between variables and make decisions in the round of results.

3. Monte Carlo simulation – is one of the most popular and useful tools for evaluation of unpredictable variables influence over specific factor by way of random numbers and data simulation. This technique has common usage in different directions (project management, finance, logistics, engineering).

Qualitative data measuring

4. Content analysis – has a form of analyzing technique that allows to understand topics of qualitative data. Also, it’s possible to identify commonest topics by the use of specific themes and ideas color-coding. The method is often used to analyze customer reviews, interview and surveys. As a consequence of this it’s easy to specify pain points.

5. Narrative analysis – helps to understand better organization culture by people stories analyzing. In this way it is possible to determinate an employees’ attitude to work and customers’ attitude to organization.

There is no «golden standard» of data analysis. The main condition of proper analyzing: the methodology has to reflect basic data and information type that needed to be obtained as a result.

We’re being chosen

«Tools developed by DataLabs are extremely impressed. It doesn’t look like QlikView, it looks like a website».

A firm Ansell (Health & Safety provider) hired DataLabs to create an analytics system to gather all its ERPs’ data. They developed and continue to host a system using QlikView to check APIs to have the ERPs’ data in a single dashboard.

Senior manager FP&A, Business Intelligence, Ansell said: «DataLabs knows the technology well, allowing them to build a very strong platform where all data is easily accessible. The tool they developed was extremely impressive. Their team is responsive, open to new challenges, and communicative».

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