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Data Analytics Trends for 2018

Using data profitably and creating added value is a key factor for companies in 2018. The world is becoming increasingly networked and ever larger amounts of data are accumulating. BI and analytics solutions and the right strategies can be used to generate real competitive advantage. Here below are listed the top tends concerning Data Analytics of 2018.

 

How new technologies support analysis

Learning (ML) technology is getting improved day by day and becoming the ultimate tool in creating in-depth analysis and accurate predictions. ML is part of the AI that uses algorithms to derive modules from structured and unstructured data. The technology supports the analysts with automation and thus increases their efficiency. The data analyst no longer has to spend time on labor-intensive tasks such as basic calculation, but can deal with the business and strategic implications of analysis to develop appropriate steps. ML and AI will therefore not replace the analyst, but make its work more efficient, effective and precise.

 

Natural Language Processing (NLP)

According to Gartner, every second analytical query on search, natural language processing (NLP) or language should be generated by 2020. NLP will allow more sophisticated questions to be asked about data and relevant answers that will lead to better insights and decisions. At the same time, research is making progress by exploring ways in which people ask questions. Results of this research will benefit data analysis – as well as results in the areas of application of NLP. Because the new technology does not make sense in every situation. Their benefit is rather to support the appropriate work processes in a natural way.

 

Crowdsourcing for modern governance

With self-service analytics, users from a wide range of areas gain valuable insights that also inspire them to adopt innovative governance models. The decisive factor here is that the data is only available to the respective authorized users. The impact of BI and analytics strategies on modern governance models will continue in the coming year: IT departments and data engineers will only provide data from trusted data sources. With the synchronized trend towards self-service analytics, more and more end users have the freedom to explore their data without security risk.

 

More flexibility in multi-cloud environments

According to a recent Gartner study, around 70%of businesses will implement a multi-cloud strategy by 2019 in order to stop being dependent on a single legacy solution. With a multi-cloud environment, they can also quickly define which provider offers the best performance and support for a given scenario. However, the added flexibility of having a multi-cloud environment also adds to the cost of allocating workloads across vendors, as well as incorporating internal development teams into a variety of platforms. In the multi-cloud strategy, cost estimates – for deployment, internal usage, workload, and implementation – should, therefore, be listed separately for each cloud platform.

 
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Increasing importance of the Chief Data Officer

With data and analytics now playing a key role for companies, a growing gap is emerging between responsibilities for insight and data security. To close them, more and more organizations are moving to analytics at the board level. In many places, there is now a so-called Chief Data Officer (CDO) or Chief Analytics Officer (CAO), who has the task to establish a data-driven corporate culture – that is to drive the change in business processes, overcome cultural barriers and the value of analytics to communicate at all levels of the organization. Due to the results orientation of the CDO / CAO, the development of analytical strategies is increasingly becoming a top priority.

 

The IoT innovation

The so-called Location of Things, a subcategory of the Internet of Things (IoT), refers to IoT devices that can calculate and communicate their geographical position. On the basis of the collected data, the user can also take into account the location of the respective device as well as the context that may be involved in the evaluation of activities and usage patterns. In addition to tracking objects and people, the technology can also interact with mobile devices such as smartwatches, badges, or tags, enabling personalized experiences. Such data makes it easier to predict which event will occur where and with what probability.

 

The role of the data engineer is gaining importance

Data engineers make a significant contribution to companies using their data for better business decisions. No wonder that demand continues to rise: from 2013 to 2015, the number of data engineers has more than doubled. In October 2017, LinkedIn held more than 3,500 vacancies under this title. Data engineers are responsible for extracting data from the company’s foundational systems so those insights can serve as decision-making basics. The data engineer does not just have to understand what information is hidden in the data and what it does for the business. He also has to develop the technical solutions to make the data usable.

 

Analytics brings science and art together

The use of technology is getting easier. Everyone can “play” with data today without having to have deep technical knowledge. Researchers who understand the art of storytelling are pursued for data analysis. More and more companies see data analysis as a business priority. And they recognize that employees with analytical thinking and storytelling skills can gain competitive advantage. Thus, the data analysis brings together aspects of art and science. The focus shifts – from simple data delivery to data-driven stories that lead to concrete decisions.

 

Universities are intensifying data science programs

For the second time in a year, the Data Scientist ranked first in America’s annual Glassdoor ranking of the best jobs in America. The current report by PwC and the Business-Higher Education Forum shows how high applicants with data knowledge and analytical skills are in the favor of employers: 69% of the companies surveyed indicated that they would prefer suitably qualified candidates over the next four years instead of candidates without appropriate competencies. In the face of growing demand from employers, it is becoming more and more urgent to train competent data experts. In the United States, universities are expanding their data science and analytics programs or establishing new institutes for these subjects. In Germany too, some universities have begun to increase their supply.

#BusinessIntelligence and Decision-Making Strategic Project

Paying attention to how your organization handles decisions rights is the first step to making the effective, timely decisions needed to perform business strategies and realize goals. But decision-making is not a stress-free situation. The uncertainty, complex and chaotic environment, limits the perception of clear signals. On the other hand, one cannot just predict all the possibilities due to the short time limit for certain decisions. In some cases, decision makers must act quickly, to take advantage of all positive breaks without wasting any time. In some scenarios decision-making can be considered as a risk-taking situation. That’s why when implementing the decision-making IT project, technological concerns tend to obscure user’s expectations of decision-making. Thus to provide an effective decision-making IT solution, experts must think about deployment of the strategy.

 

decision-making to Business Intelligence

The formulation of “Business Intelligence” naturally came from the expression of “decision support system” which, although a little dated, was nevertheless much more expressive. A decision-making IT project is equal to build a technological IT architecture to facilitate and support decision makers in any organization. It is clear and concise. Yet, sometimes in practice, the last part of the formulation, the term “decisional”, has all too often been shortened. The “decision-making computer project” is then a “computer project” where only the implementation of technology matters. The designers seem to adopt the hypothesis that it’s enough to work according the rules of qualified technology as “decision-making tools”, without really caring about the purpose of help to the decision.

There’s no doubt that connecting heterogeneous systems, collect and integration of data in multiple formats is a constant headache. Data collection phase is not a fun part. Plus when it’s badly committed, with a minimal budget quickly set, the complexity of this essential phase can soon send the entire project to the trap. In addition to previously said, the exponential expansion of IOT, multiplies the points of access to the system which does not, in any way, solve the problem. Having said that, companies must think about a strong strategy before working on any kind of decision-making IT projects.

 

A strategic project:

 

How to define the assistance to decision-making procedure in company if it’s not in close relation with the deployment of the strategy? Decision-makers do not make decisions all the way, depending on their mood of the moment. They follow a precise direction, each in its own way according to its context, but the direction is common shared based on figures and facts. It’s therefore from the formulation of the strategy that one must start to define the broad lines of an intelligent decision-making system.

 

The dashboard – at the heart of the process:

 

Now a majority of company players are required to make ad-hoc decisions in order to accomplish their daily tasks. To ensure that all the necessary assistance is available, the designers need to focus on the needs of decision-makers:

 

  • What types of decisions are needed to achieve the strategic objectives?
  • How do they measure the risks?
  • What information should be available as soon as possible so that they can make advantageous decisions?
  • Finally, more generally, what are the needs of each decision-maker for presentation and analysis tools?

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This is finally the purpose of the decision-making IT project in full light. Therefore it shouldn’t be a catalog of tools, stacks of report, but a personalized dashboard system in its own way. The design of the dashboard of each decision-maker must be at the heart of the decision-making project.

 

From decision-making to Business Intelligence: 

 

We can now safely adopt the term “Business Intelligence”, whose role is to ensure the fair flow of consistent and consolidated information flows between decision-making nodes. Business Intelligence is still only in the beginnings of its dawn. The predicted evolution towards the generalization of the storage and processing of very large masses of data risks to shift once more the focus on the technical aspects at the expense of the decision-making process. The designer must not lose sight of the demands of decision-making process, in a complex and uncertain universe, in order to better value the role and importance of tools.

 

#BusinessIntelligence: for a better Control of Data

Business intelligence (BI) is a subject in full evolution, addressing the general management as well as the trades. BI helps decision-makers to get an overview of the different activities of the company and its environment. This cross-sectional view requires knowledge of the various business lines and involves certain organizational and managerial specificities. From the exploitation of business data to IT governance, the Business Intelligence point of view, and its decision-making tools such as reporting, dashboard and predictive analysis are so important for the success of a business.

The organization of BI in the company is highly dependent on the organization of the company itself. However, BI can have a structuring impact for the company, notably through the formalization of data repositories and the setting up of a competence center.

What is the purpose of Business Intelligence?

 

Business Intelligence (BI) encompasses IT solutions that provide decision support to professionals with end-to-end reports and dashboards to track analytical and forward-looking business activities of the company.

 

This notion was appeared in the end of the 1970s with the first infocentres. In the 1980s, the arrival of relational databases and the client / server made it possible to isolate production computing from decision-making devices. At the same time, different actors embarked as specialist of “business” layers analysist, in order to mask the complexity of the data structures. Beginning in the 1990s and 2000s, BI platforms were built around a data warehouse to integrate and organize information from enterprise applications (extraction, transfer and Consolidation). The only objective was to respond optimally to queries from reporting tools and dashboards of indicators and made it available to operational managers.

 

How does decision-making tools work today?

 

Over the past few years, BI platforms have benefited from NoSQL databases, enabling them to directly process unstructured data. Today Business Intelligence applications benefit from a more powerful hardware architecture, with the emergence of 64-bit, multi-core, and in-memory (RAM) architectures. In this way, they can execute more complex processes, such as data mining and multidimensional analyzes, which consist in modeling data according to several axes (turnover / geographical area, customer, product category, etc.). ..).

 

Which fields are covered by the BI?

 

Traditionally focused on accounting issues (consolidation and budget planning), BI has gradually expanded to cover all major areas of the company, from customer relationship management to supply chain management and human resources.

 

  • Finance, with financial and budgetary reports, for example;
  • Sale, with analysis of sales outlets, analysis of the profitability and impact of promotions for example;
  • Marketing, with customer segmentation, behavioral analysis for example;
  • Logistics, with optimization of inventory management, tracking of deliveries for example;
  • Human resources, with the optimization of the allocation of resources for example;

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Specialized publishers have developed ready-to-use indicator libraries to monitor these different activities. Finally, with the emergence of new web technologies (including HTML5 and the JavaScript and AJAX graphical interfaces) we’ve seen the appearance of new players proposing a BI approach in the cloud or SaaS mode.   

 

Today, information is omnipresent; the difficulty is not to collect it, but to make it available in the right form, at the right time and to the right person, who will know how to exploit it and drive added value. So the BI market offers fairly comprehensive and complete solutions for the data reporting and consolidation aspects of both proprietary and open source domains. Possible developments in the short to medium term would include proactive and simulation analysis tools as well as the interactivity and user-friendliness of data access and the combination of structured and unstructured data from Internal and external data.

How you can benefit from using Big Data in your business operations

Big Data, it is above all a great opportunity for companies to innovate, grow their sales, profits and markets so they can grow their portfolio and also to create new jobs. For customers and consumers of these businesses, it is a guarantee of a better customer experience in all interactions with brands either in sales, marketing or at the customer service.

 

The idea of data creating business value is not new, however, to explain briefly why, today, only a “small portion” of the data – often digital marketing or customers data but also data collected from connected objects, various maintenance processes, product usage statistics – are actually exploited by companies. The main reason is that the collection, storage and operating correctly the gathered data cost very expensive for some companies. In fact more expensive than the apparent or immediate value generated by the exploitation of these data. Another reason is the fact that most organizations still aren’t enough competent to technically manage the huge flow of data that is generated by the above examples.

 

The big data approach is used to collect, store, and analyze all the data at much more reasonable cost than the traditional process thanks to new storage technologies. Big data technologies release organizations from the traditional accuracy vs. cost challenge by enabling them to store data at the lowest level of detail, keeping all data history under reasonable costs and with less effort if managed correctly.

 

BIG DATA

The benefit is that we can now consider to analyze and turn into actionable information, all the data that was once out of reach for both technical and financial reasons but the density of information contained in this new data is still less compared, for example, to conventional transactional data. But this problem is somehow compensated by the huge volume of data first, and especially the ability to cross-check them through statistical algorithms. So now, with a little help of experts in domain, companies have now the ability to exploit big data and to transform it into actionable knowledge then in make profit out of it. “The ability to exploit data correctly will define the difference between the losers and winners going forward,” says Tim McGuire, a McKinsey director.

 

In today’s hypercompetitive business environment, one of the biggest business challenges for companies is the importance of being able to find and analyze the relevant data they need, they must find it quickly.

 

Here a video that explains what’s Big Data in detail.

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Now that you’ve saw the video and read the above part, let’s check example of a company that has successfully used THE Big Data. ACCOR, one of the French leaders in the hotel industry, has significantly increased its hotel booking rates through the use of these techniques. The business challenge is based on real time offers and recommendations for online users company’s website.

 

Accor has always wanted to derive insights from information in order to make better, smarter, real time, factbased decisions and now thanks to the RTD solution of Oracle (Real Time Solution), ACCOR group achieved more benefits than before. The project is recent (2013) and is still rising and making benefits for the group.

 

In today’s highly interconnected global business environment, marketing departments are front of the most exciting challenges of Big Data in which customer insight is a top priority for organizations in all industries. The main challenge here is to develop, maintain and control a 360 ° view across all channels and points of interaction with the customers.

 

As Customer satisfaction is at the heart of the selling process for all companies, they must organize the marketing and operational processes around the data. This data must be transformed into knowledge by the analytical part of big data, especially must be operational and available for customer interaction usage.

 

What’s the future of Big Data?

 

futur of bigdata

The pursuit of information has been a human preoccupation since knowledge was first recorded. Talking about the future of big data is really vague and deep topic, because it’s very much a “here and now” phenomenon. Big Data is a major trend and is set to offer companies tremendous insight. Gartner describes big data as a situation where the volume, velocity and variety of data exceed an organization’s ability to use that data for accurate and timely decision-making”.
Organizations are increasingly realising the utility of data that is bringing the value through continuous improvements in their existing operations.

 

Many uses of Big Data are only in their infancy and we can expect to see a surge of uses that we have not even thought of yet today.

 

The Big Data is as important for business as the Internet in its time. Companies that won’t act now will may be overtaken in few years by the competition. It is best to start even a modest project quickly rather than waiting for the technologies and methods to stabilize because then it will be too late.

 

Big Data contribution in Human Resource management

If Big Data was initially used in advertising, finance and marketing sector, today other sectors are looking to take advantage out of it. More like marketing sector, the HR function is undergoing a revolutionary transformation.

 

Big data and artificial intelligence have become major player to the transformation of any organization and are major elements of innovation, whether they concern material or human assets, data and algorithms are increasingly taking place in organizations to optimize business processes.

 

Not for a long time, but the HR sector is finally concerned by this trend and using algorithmic intelligence to improve performance. Companies are able to make an incredible amount of data analysis around selection and recruitment processes to identify potential candidates. The focus on data is a real winner for HR if handled correctly.

 

Started from the most dynamic and innovative companies in the world, now traditional recruitment processes are increasingly being replace by large-scale data analysis. Basically the data is used to anticipate HR department needs: crossing (potential) employee competences and business strategy. It is easier to know when and where the company will need an employee, what position and what mission. Thanks to Big Data input, companies can reduce the risk of failed recruitment of more than 20%.

Let’s take an example of BrightOwl, an online platform, created by Xorlogics, that matches experts with jobs in the clinical research and life sciences industry. It’s an innovative company working at the intersection of data and recruiting helps job-seeker through the application and hiring process and helps companies to receive an overview of applicants available for desired profile via matching the right skills to their projects.

 

Before implanting big data in your HR management, you should know the two major benefits of it which are relate to sourcing candidates and managing employees.

  • Sourcing is the most important part in use of Big Data. It can analyze resume keywords, skills and candidate profiles on online platforms. These collected data analyzes candidate’s profiles to match them as much as possible to the offer and the company’s values.
  • The second element relates to manage employees, it is a powerful internal development tool. It helps to develop the skills of your best employees. By analyzing your company’s internal data, the Big Data offers custom targeting training to employees.

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Big Data therefore requires extensive preparation before its integration into a business. HR departments must be trained in the new tool and accept it as providing new features to their posts. But once past integration, big data will open many opportunities for HR functions, which can be positioned in the center of the digital transition of the business.

Business Intelligence Insights

Business Intelligence development in 2016

Strategic information is often embedded in the volume of data information systems. In addition, these data are sometimes stored unorganized or in unstructured way. The volume of data is growing daily, it is very difficult to treat them and understand their meaning. But access to information, all information, without any delay, that’s what leaders want and that’s what BI is offering. Since past twenty years BI or “Business Intelligence” has seriously made its entry into business. But what is this software solution? Who is it for?

 

In a few words: BI consist to transform data into information in order to predict the future development of any company. This is an IT approach that generates and processes data in order to enable leaders to have a global vision of the results of their business. Those data generated reports provides them a better analysis and understanding of the market. It is a real help in strategic decision making. Gartner findings revealed that the market for BI solutions has an annual growth of 7%.

 

Few years back, the BI was mainly used in financial sector for different departments such as accounting, management control etc. because financial institutions need to support business activities and decisions making in time. But now we all are witnessing a BI technological change and development means providing the opportunity of significant competitive advantage for all organization. Expansion of BI is having such a huge success that now it’s available to any size of organization. Not only in mode but the BI now, is a necessity of all organizations, it represents a real strength of company.

 

Moreover, the growing use of cloud-services clearly indicates that more and more companies choose not to waste their budget in purchase of expensive and complex software. They prefer the affordable price of services available in SaaS mode, simple and quick to implement, without cost to install or setup + access data any moment. These solutions especially have the enormous advantage of being a payback within months.

 

In a company, data presentation tools offered by Business Intelligence are for everyone. Every department will find the tool that suits them best and benefit for its function.

 

  • Management monitors business performance and effectiveness of their strategy via performance indicators presented in dashboards.
  • Analysts literally navigate in information to understand and analyze the highlights of any organization.
  • Finally, employees use structured reports to follow and adapt the day to day operational activities of the company.

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There’s no doubt that the collaboration of these services available in SaaS mode, offers companies the opportunity to create a fluid flow of information. A well-designed BI solution provides a consolidated view of key business data which isn’t available anywhere in the organization, gives management visibility and control over measures that otherwise wouldn’t exist.

 

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