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Skills Needed For Analytics
The lack of the necessary skills, quite widespread today in all ICT sectors, in the field of Business Intelligence is linked in particular to three figures, according to what Vercellis explains:
- Technological: People with mathematical and algorithmic skills, mainly aimed at the theme of machine learning and Artificial Intelligence
- Connection: Business translators, or “translators” of the problems that arise in the context of the business and can transform them into specific tasks to be proposed for data analysis
- Business-oriented: The Business analyst who identifies problems within the various parts of the company and brings them to the attention of the people responsible for solving them
These roles mustn’t be considered watertight, but the skills of the people who perform them must at least partially overlap to communicate effectively. How to do it? Vercelli proposes job enrichment and job rotation among the possible solutions, but above all, more comprehensive training of the various elements can give a broader vision that goes beyond the individual figure.
Application areas: Web analytics and more
In a data-driven society, the application areas of data analytics are many. Those below collect the most representative areas.
Web Analytics and Marketing Optimization
From the creative process to the highly data-driven process: marketing is a significant user of the most advanced analytics technologies. Marketing organizations use analytics to determine the results of campaigns or efforts and guide investment decisions and consumer targeting.
Demographic studies, customer segmentation, joint analysis, and other techniques allow marketers to use large amounts of purchase, survey, and panel data to understand and communicate marketing strategy.
An essential tool for marketing optimization is Web Analytics which allows you to collect session-level information on a website. By analyzing these interactions, it is possible to track the referrer, search for keywords, identify the IP address, and track visitor activity.
With this information, marketers can improve their marketing campaigns, creative website content, and information architecture. Analysis techniques frequently used in marketing include marketing mix modeling, price and promotion analysis, sales force optimization, and customer analysis, such as segmentation.
Using the web analytics tools, you can analyze your digital platforms with an excellent degree of detail, benchmarking with data from the market or with other organizations and sites in the sector.
What is Google Analytics?
Several free and paid tools are used to do Web analytics. Among these, Google Analytics is the free service offered by Google that allows users to analyze visitors’ behavior to a website, provides useful statistics for Webmasters and those who have created or want to carry out marketing campaigns on the Internet. It is currently the most used tool to check the duration of the visit sessions to the systems, the most viewed pages, the origin of the visit.
Google Analytics can be integrated with Google Ads to analyze online campaigns, monitor their quality, if interactions have been made, etc.
What is People Analytics?
People analytics is applied explicitly to human resources. Through the analysis of behavioral data in addition to the classic ones relating to training, CV, etc., the goal is to understand which employees to hire, which to reward or promote, which responsibilities to assign, etc. Human resources analysis is becoming increasingly important to understand what kind of behavioral profiles would succeed and fail.
For example, an analysis may find that individuals who fit a specific type of profile are those most likely to succeed in a specific role, making them the best employees to hire. Using people analytics can have a spectrum of comprehensive action: from the analysis of sales productivity to that of employee turnover and retention, from that of accidents and fraud to that which allows us to understand which employees can determine greater customer loyalty and satisfaction.
What Is Portfolio Analytics?
It is a widespread application for banks and credit bureaus to both balances the loan yield with the risk of default for each loan: the accounts collected by the bank can differ according to social status (wealthy, average, week, etc. ) of the owner, geographic location, its net worth and many other factors, the use of portfolio analytics allows you to cross and analyze all data by combining time-series analyzes with many other issues to make decisions on when to lend money to different segments of borrowers or interest rate decisions charged to members of a portfolio segment to cover any losses between members in that segment.
What Is Risk Analytics?
Predictive models in the banking sector are developed to ensure certainty in risk scores for individual customers; therefore, an analytics application can be partially overlapped with the previous one even if it has a broader spectrum of action. It is used, for example, to analyze whether an online or credit card transaction can be fraudulent using data relating to the customer’s transaction history.
What Is Security Analytics?
It is about analytics techniques for collecting and analyzing security events to understand and analyze the events that present the most significant risk. It is one of the areas of data analytics of most significant development.
Alongside the risks caused by malware threats or known vulnerabilities, which could heavily mitigate with regular antivirus and timely patching of applications, there are also more sophisticated and persistent ones that require the ability to capture and analyze “weak signals.”
Among these, for example, data traffic that appears to be expected but which, on the other hand, when properly examined, turn out to be anomalies that can constitute the antechamber of actual attacks.
Cybercriminals make extensive use of analytics to launch their attacks: thanks to social engineering, it is much easier to make a user fall into the phishing trap to allow an APT ( Advanced Persistent Threat ) attack.
Hunting for hidden and persistent threats, continually monitoring data traffic on networks, and identifying anomalous user behavior is Security Analytics’s goal.
Data analytics in a strictly technological field, to model a ‘next generation’ IT Service Management is another vital growth area.
Analytics tools make it possible to extract useful information, from the myriad of data available, for IT, particularly to intervene effectively on Service Management.
In particular, there are some compelling use cases’ that produce value both on the IT level and towards the user/consumer:
- It Service Analytics, tools with real-time analysis functions that provide maximum visibility and transparency on the relationships between business ‘transactions’ and the behavior of the applications and IT infrastructures supporting these services; the value of IT lies precisely in this visibility and analytical capacity, the value for the business user (the primary IT customer) lies in the most effective alignment between business and IT and, consequently, in the best quality of service received.
- Interaction Analytics, handy tools for the Service Desk: analytical tools allow you to correlate all the data relating to the IT services through which the user interacts (profile, history of events, accesses, business data, use of applications, requests for intervention, and support, etc.); the analysis of these data provides valuable information to improve the Help Desk service, for example, but also to automate any processes such as support interventions.
- Problem Analytics, in this case, the tools allow to carry out correlated analysis of events in real-time by automating any intervention processes and enabling a ‘preventive support’ system, allowing the IT to proceed even before the user can perceive a decline in IT service. This results in a better quality of service, greater response capacity, and reduced intervention times.
- Capacity Management, in this case, the analytics tools allow IT to dynamically model IT resources more effectively, in particular, according to the real needs of users and the IT resources necessary to ‘feed’ the services provided.