In July 2017, the concept of Augmented Analytics was first introduced in a research published for Gartner. The topic immediately aroused interest and immediately became an impact trend in the field of Business Intelligence, so much so that just one year later Gartner published the report “Hype Cycle for Analytics and Business Intelligence“.
In its first definition, the concept of Augmented Analytics is described as a new data analysis approach that exploits Machine Learning and natural language (NLG) technologies, in order to automatically identify the most relevant results and independently suggest the concrete actions to be taken. Not bad as a goal …
A new approach
In fact, one of the main current problems is the fact that data tend to become increasingly numerous and increasingly complex. Often it is not possible to integrate them quickly also because of their extreme complexity. The risk of losing information becomes therefore very high, and precisely this complexity does not allow us to explore all the possible opportunities offered by the information available to us. It also increases the likelihood of having as a result just what we “looked for” and not what we “could find”.
We add the fact that, to date, all data extraction and transformation activities are still completely manual (“Artisanal” area of the figure shown below), considerably increasing the possibility of error. Furthermore, finding a data scientist today, a relatively recent profession that requires skills in information technology, statistics and economics, and for which there are still few studies, is really very difficult.
It therefore seems natural that the introduction of analytical tools capable of interacting with the human being in its natural language and autonomously identifying the most significant data without the mediation of analysts and without human intervention in general, becomes a fundamental key for the future of Business Intelligence.
The traditional approach
He is currently approaching traditional projects involving various figures such as analysts, computer scientists, data scientists and managers, involved in the usual decision-making activities that will then lead to the final request. This is followed by various data extraction and cleaning activities performed by computer scientists or technicians with specific skills. We will try to put together these data by linking them together and only then transforming them into information that is more synthetic and “decisive”.
As we can easily imagine the costs and times of such an activity are absolutely remarkable.
The Augmented Analytics approach
With this new approach we try to centralize all the procedures in a single solution, from data collection to analysis processing, to monitoring results. Machine Learning and Artificial Intelligence algorithms are used to automate the procedures making data analysis easier. Billions of data combinations are automatically analyzed, automatically finding correlations and identifying any predictive scenarios.
The future of Augmented Analytics
According to the slide (source Oracle Corporation), there are several levels:
Level 0 – Artisanal: everything is handmade, as in the classic approach. The data model and reporting are the responsibility of IT.
Level 1 – Self Service: data management is still largely manual, but human interaction with data will be done with the natural language (Natural Language Query). Visualizations and graphs will be suggested based on the data we are querying.
Level 2 – Deeper Insights: the first phases of advanced data management are displayed (recommended sources, joins, crowdsourcing suggestions, intelligent cataloging) and increased navigation helps to discover information that would otherwise require a strong human effort.
Level 3 – Data Foundation: data management is increased, corrections and enrichments are automatically identified. New views and new data sets are added.
Level 4 – Collective Intelligence: the system learns metrics and KPIs that alert you when they require your attention. You have both company KPIs and system KPIs. Insights become pervasive, commercial intent passes from an idea to a reality, results are expected, actions are recommended, but humans continue to act.
Level 5 – Autonomous: everything becomes really guided by data, with the best subsequent actions performed on the basis of forecasts, insights and intents. The system is the engine of change.
Where are we at?
I believe that for the moment we have stabilized between the third and the fourth stage, data management starts to be really “augmented”, it is becoming easier to integrate and manage a large amount of information and take the first steps towards automatic identification of KPIs.
Completing the fourth stage and finally reaching the fifth, the “autonomous” total, will be the challenge of the coming years. The way of conceiving Business Intelligence will change drastically, but the challenges to be faced are always more beautiful. We are ready?
In the analytics business and in the business world — business intelligence, analytical devices and BI applications are the main discussed subject. BI is quicker and progressively precise in reporting; data analysis is increasingly vigorous, and forecasting has changed essentially and developed massively. An ever-increasing number of companies have begun to value the worth of business insight (BI) with regards to their decision-making process. The previous couple of years have seen BI systems experience various advancements. With the rising multifaceted nature of the business intelligence environment, the distinguishing proof of trends and market improvements is a key factor in powerful decision-making. It is progressively imperative to utilize the most recent innovations and methodologies so as to adapt to digitalization and market competition. Let’s look at top BI trends that will dominate 2020.
We've all heard a bit about it now: the Data Scientist, for a few years now, is one of the most sought-after pre-treatment figures. Who exactly is he and what exactly does he do? Here are some answers we can find on the web:
What does the Data Scientist do?
A bit statistical, a bit itist, a bit of an economist, but also a marketing expert and a communication enthusiast. He is the data scientist, the job that the Harvard Business Review defined as the "sexiest profession of the 21st century" in 2013. Is he a required professional figure in Italy? And how do you become a data scientist?
According to Claudio Sartori, Scientific Director of the New Master in Data Science of Bologna Business School in an interview reported on sarce.it it is a figure that "requires multidisciplinary skills, because it must not only select, analyze and interpret an ever-widening and complex body of data, but also find the best way to make the processing and results available to the structure for which it works, whether it is a company or a public administration."
The data scientist must first put order in the data, then wonder where his organization wants to go, what information can be useful for his strategy. Finally, it must know how to do but also know how to communicate, make available to management the results of what it has done. The most sophisticated analyses are only useful if they are properly transmitted to those who have to make the decisions, then understood and used to achieve the desired results." […]
In short, the primary task of a data scientist is to explore the data, starting with precise business demands. He is a real investigator and puts all his analytical creativity on the field. Armed with technological tools and machine learning algorithms, he is able to scientifically examine and predict correlations between phenomena that at first analysis are invisible. Its goal is to get insights as accurate as they can give the business an accurate overview of the problem to be solved.
Who is the Data Scientist?
An attempt was made to profile the Data Scientist in the Italian company, the results were reported in the report "The new professionalism and skills for the management of big data", prepared by the Digital Innovation Observers of the Polytechnic University of Milan:
Work within the IT division, an ad hoc business function, or within one of the pre-existing functions. He is a graduate, mostly master's degree, who has often taken training courses in statistics and computer science. He has a background of skills focused primarily on machine learning, analytics and Knowledge Deployment. And he earns on average about 67,000 euros a year, with a bonus that generally stands at 10% of the salary.
It is clear that a strong heterogeneity of skills is essential, from business to programming to technology and especially for the trio of Machine Learning, Analytics and Knowledge Deployment. From a training point of view, it boasts a course of study concluded in the majority of cases (50%) with a master's degree (engineers, economics and computer science go for the most) and accompanied by specialization courses (statistics, computer science and management the most chosen).
The Data Scientist in Italy
Data Scientist is now present in 3 out of 10 companies, but the number of specialists employed full-time is growing at an annual rate of 57%. A sign of a growing corporate sensitivity to the new challenges of the big data boom.
According to data from Robert Half's Technology and IT Salary Guide for 2018, a data scientist's average salary can range, depending on experience, between 100,000 and 168 thousand dollars per year.
Who needs a Data Scientist?
Each sector has its own wealth of data to analyze. Businesses need to analyze their data to make decisions about efficiency, inventory, manufacturing errors, customer loyalty, and so on. In the e-commerce sector, recognizing trends to improve the proposal to the customer, in the financial situation the data of transactions are fundamental assets, but also in the Public Administration you can monitor the general satisfaction of citizens. Health, communication and social networking are other areas where the needs are evident.
Il Sole 24 Ore also reports that in Italy less than one in three large Italian companies has noticed the need to have a Data Scientist inside. Among Italian SMEs, only 34% of these are in a budget dedicated to Analytics.
This figure is referred to as a professional able to range between technical, computer, economic and statistical skills. It's a kind of evolution from the Business Intelligence figure to the Data Scientist. Whereas in the first case you tend to collect requests from the business and then return numbers in output, in the second case it is the Data Scientist who "proposes solutions" starting from a mostly generic input of the business. It collects information on its own and analyzes its correlations, creating new algorithms and applying machine learning techniques.
“Machine Learning has been a revolution as it was the Internet”
With this statement Larry Ellison opened the 2017 edition of Oracle Open World and with this statement introduced the new Oracle Database 18c, the world’s first database that manages alone, the first Autonomous Database.
At Oracle OpenWorld 2017, Oracle Chairman of the Board and CTO Larry Ellison unveiled his vision for the world’s first autonomous database cloud. Powered by Oracle Database 18c, the next generation of the industry-leading database, Oracle Autonomous Database Cloud uses ground-breaking machine learning to enable automation that eliminates human labor, human error and manual tuning, to enable unprecedented availability, high performance and security at a much lower cost.
An interesting article published on Forbes. I bring you some extracts:
For decades, SAP has been the world leader in enterprise applications and Oracle has been the frontrunner in enterprise databases, with both companies retaining their leadership positions in spite of the seismic shifts in the industry caused by the moves from mainframes to minicomputers to client-server to the Internet.
Oracle’s recent surge in cloud-applications revenue—it sold $1 billion in SaaS apps during the quarter ended May 31 and $3.4 billion for the year—give it a legitimate chance to overtake SAP as the world’s #1 provider of enterprise applications, particularly in the massive cloud ERP market.
But growth is not over:
Triggering that steep growth, said Hurd, were 868 new Cloud ERP customers in Q4, plus 200 more “expansions” from customers that were added more of the Oracle Cloud ERP services to ones they’d purchased previously.
Oracle also focuses on the completeness of its solutions:
Indeed, it was the ERP business that powered SAP to prominence in the enterprise-applications space over the past 30 years as it dominated the global market for software that helps companies manage finances, run supply chains, oversee purchasing and more.
And while Oracle has been offering a full set of Cloud ERP services for only a handful of quarters, SAP didn’t introduce the SaaS version of its flagship ERP franchise until early this year—giving Oracle a few quarters to build up considerable momentum.
Outlook predicts Oracle’s leader in SAP in ERP cloud solutions:
So as long-time competitors Oracle and SAP square off in this new and strategically vital category, the evidence I’ve been able to gather so far tells me that Oracle is the clear leader over SAP so far in the cloud ERP space. And here are a few things we know and don’t know:
- We know SAP fully understands how essential it is for the company to win in the cloud ERP space. And we don’t know just how well that 6-month effort is going.
- We know SAP has thousands of longtime on-premise ERP customers across the globe, and we know that Oracle’s cloud ERP team will be doing everything in its power to woo those companies away from SAP when those businesses move their ERP systems to the cloud.
- We know Oracle posted about $300 million in cloud ERP revenue in its most-recent quarter, growing more than 150% and creating the $1.2 billion annualized run rate cited by Hurd. We don’t know what type of revenue S/4HANA Cloud is currently generating.
- We do know SAP’s Roos is extremely bullish on his cloud ERP prospects: “We provide a high level of flexibility and extensibility via the SAP Cloud Platform and none of our competitors are offering the same level of integration between cloud apps,” Roos said in his email reply to my questions. “In May, we released significant enhancements for large enterprises including discrete manufacturers, and we’ll continue our rapid quarterly innovation cycles with our August release, which will, among other things, bring Treasury & Risk Management, Contract and Lease Management.”
- We know that business customers will be huge winners in this latest Oracle-SAP shootout because both of these exceptional tech companies know that unless they deliver superlative products at attractive prices, the other will prevail.
- In his concluding email remark, Roos said, ” We see Oracle and we compete with them. And as more of our customers go live and put our Cloud ERP at their core, I’m confident this will be a true runaway story for SAP.”
Read the complete article for all the details!
Oracle released the 2016 IOUG Survey on Database Cloud, in wich analyze how peers are responding to new cloud database solutions.
OUG Survey on Database Cloud: how your peers are using new database cloud technologies and how these services could help you improve workload efficiency and flexibility.
Read the Report here.
Oracle Synopsis is now available for Android and iOS!
Oracle Synopsis does more than just open spreadsheet, it automatically aggregates the data and tells the story behind the numbers, with rich graphics. It’s easy to use and free. To get started:
- Download the application on your mobile device
- Open the data source (such as a spreadsheet from an email)
- See—and explore—important insights from your data
Oracle Synopsis includes the following features:
- Smart summaries
- Automatic chart generation
- Ability to filter, sort, and zoom in on data
- Fingerprint lock
- 100% on-device analytics
- Works with iOS and Android devices
- Works with Excel and CSV files
To disable User Account Control (UAC) in Windows Server 2012 & Windows Server 2012 R2 open Control Panel -> User Accounts, click on Change User Account Control settings and select Never notify:
But in fact it is not so simple. After installation and configuration of some software, I noticed that disabling UAC via Control Panel did not help me.
To permanently disable the UAC, you mast set the registry key EnableLUA under HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System to 0x00000000 (0) as per the following MSDN article here. Following a reboot, UAC was completely disabled and all worked correctly.
After I successfully applied the patch EPM 220.127.116.11.500, I tried to login Workspace but I had a bad surprise:
“An error occurred processing the result from the server
Description: An invalid content type ‘text / plain’ was found INSTEAD of [text | Application] / xml ‘. “
I think this is a problem created by temporary files… then I did the following in the EPM Configurator utility:
– configure web server
– configure logical web address
– redeploy EPMA web
rebooted the server and started the services. I then could login to Workspace without any issues…