2021 Gartner Magic Quadrant for Business Intelligence & Analytics - Summary

Looking for 2021 Gartner Magic Quadrant about Augmented Analytics & Business Intelligence platform ? This is the right place !

We will provide you an insightful summary of the 2021 Gartner Magic Quadrant as soon as it published.

Gartner Magic Quadrant Report Definition of Analytics and BI Platforms

“Augmented analytics and business intelligence (BI) platforms are characterized by easy-to-use tools that support the full analytic workflow — from data preparation and ingestion to visual exploration and insight generation.”

2019 UX Procurement : what do buyers (really) think of their purchasing software ?

For the past three years, the Purchasing Solutions Exhibition has been an opportunity to publish a survey conducted among approximately 350 buyers on their expectations in terms of purchasing software. Philippe Grange, director of the "Revelations UX Purchasing 2019" study conducted with Décision Achats, in partnership with the CNA, PWC and Media Dell'Arte, compared some software editors with the survey results.

Procurement SOFTWARES: A GROWING MARKET, WITH RISING EXPECTATIONS.

The market for procurement management softwares is estimated at more than $20 billion. It is rapidly growing and has emerged as one of the most dynamic sectors of the SaaS software market over the past 5 years. Mastering these complex software solutions requires a strong skill set for buyers, despite the rise of user-friendly interfaces in the SaaS revolution. Does the sector's growth and the editors' roadmap meet expectations? 65% of buyers who responded to the survey belong to companies of more than 2,000 employees, most of whom work in a purchasing department. Only 5% of buyers are part of a business department.

TOOLS INCREASINGLY ESSENTIAL...

Patrick CONQUET, VP Product Management emphasises that the survey figures are below IVALUA's adoption rate: 66% of users log in daily, and 30% once a month (IVALUA has the highest retention rate among e-procurement solutions). 

It is therefore reassuring to see that there is a good overall adoption rate of purchasing softwares!

Some features are still missing.

On-the-ground reality is quite different. Users es utilisateurs peinent à accéder aux tableaux de bord, souvent pré-configurés et qui ne contiennent pas le niveau de détail requis pour répondre à des questions précises sur le vif.

Half of all buyers are satisfied with their tool, but 21% find it obsolete or not optimised enough! 

Overall, the e-purchasing tool is considered secure with easily shared data, but the "digital" dimension is clearly considered as lagging behind, such as: the lack of integration with other tools, lack of multi-device compatibility and lack of automation linked to recent advances in AI… Anne TESSIER, from Synertrade, confirms and approves of the users' demand for flexibility. 

However, be careful "to distinguish between a "simple" access to a webapp thanks to 4G, and a real mobile app development as we have done for our suppliers”.

A product’s ease of use remains essential to become widely adopted.

End-users hardly involved by software companies…

To the question "Have you been in direct contact with the software company in order to adapt the tool to your personal needs? ", the answer "not at all" is the most predominant. 

The majority also indicates that they were not involved in the choice of their solution beforehand. However, they consider themselves to be fully informed about the benefits of the solution they are using. This difference can be explained. The buyers interviewed are the end users of the software, but not necessarily the customers and direct contacts of the software editors.

It is difficult to get all stakeholders involved, especially in large organisations, though this is the case for the majority of buyers interviewed for this study. Patrick DE COUCY, Managing Director at JAGGAER believes that it is unrealistic to respond to pain points individually. The real power of an e-purchasing solution is to cover as wide a range of needs as possible, avoiding elements the could cause the system to break down.

... But eager for new. 

Yet, buyers have a very clear view of what’s to be done! When asked about what their priority investments would be if they were decision-makers, the buyers chose (in order and out of 10 proposals):

• adding new features for improvement

• investing in RPA and process automation solutions

• training and modernising the tools of the purchasing function

• drastic change of software editor / suite 

• support, recommendation and AI input.

Buyers want to broaden their scope, and are just waiting to be better informed about the possibilities offered by the latest RPA and AI technologies. But above all, AI is seen as an essential evolution for the 2nd year in a row (see UX Purchasing 2018). 

About 15% of buyers require direct support: what about the virtual assistants mentioned in 2018?

This year, not a single word has been said about it… However, number of innovations were present at the fair such as askR.ai, the purchaser data assistant.

>> Discover how askR.ai gives suppliers detailed data in less than 2 seconds at Technicolor Procurement Department <<

Procurement chatbot: is accessing your supplier data in less than 2 seconds for real ?

Thanks to natural language processing (NLP) AI, buyers could work without Excel.

Procurement: AN EXCITING JOB, A TIME-CONSUMING REPORTING.

A buyer does not make purchases by pure luck. It is an area where one appreciates the constant challenge of negotiations, global strategic thinking or the fact of having concrete, quantified objectives to meet over different deadlines.

In the supermarket sector for instance, it is the wide range of tasks that appeals to junior buyers. Reporting, much less. It is, however, an essential aspect in the work of a buyer, because the proper maintenance of a monitoring table enables them to measure their objectives and quantify their needs. From receiving global data, formatting KPIs and reporting back to their manager, a junior can spend between 4 to 7 hours a week gathering the right information.

DATA: LONG TRAINING SESSIONS FOR BUYERS

As the volume of data grows and becoming increasingly complex, technical proficiency in procurement is expected to become highly important. Proof of this can be seen in the few ads selected below: data literacy is an asset for any buyer. (A rather advanced requirement in this job post to integrate the Supply Chain department where SQL skills are required)

It is therefore essential to train buyers not only to use Excel but also to use an ERP specific to purchasing, not to mention data visualisation tools such as Tableau or Qlik.

One would almost come to confuse the in-depth work on data, which is the prerogative of a dedicated data team, with the "simple" understanding of KPIs or the "simple" access to supplier data without prior filtering, vital to buyers.

Cédric Le Savéant, VP Digital Office Sourcing & Supply Chain Transformation at Technicolor, clearly explains :

"In a general reflection on data access at Technicolor, you need to be able to get answers without having to systematically train employees about pivot tables! “ Source: Technicolor: a databot to access Procurement KPI

NATURAL LANGUAGE PROCESSING TO ACCESS procurement core DATA

Category management typical questions to askR.ai

Category management typical questions to askR.ai

 Free Code Camp offers a simple reading chart to measure the impact of a "chatbot as a service". For a bot capable of speeding up low value tasks and embedded in a familiar work environment such as the Slack data assistant, two questions arise: is your bot a source of savings/income? Does your bot improve your reputation?

• Is a data assistant provided on purchasing data a source of savings? YES.

At a rate of 30 min per day x 253 working days, for an average hourly wage of €17.3, it is €2188 per employee per year. Today, this is the case for GRTgaz, which saves 30 min per day and per employee thanks to askR.ai.

• Does a data assistant improve your reputation? YES.

Maybe not in terms of customer satisfaction, but in terms of employee experience, yes. A professional assistant available 24/7 that saves you time on boring tasks? That's just amazing!

Buyers need more intuitive tools to make it easier to get started, without leaving accuracy and performance on the side. An askR.ai data assistant, based on natural language processing, ensures a quick start and almost instant access to key data in a buyer's daily activity, without the need for Excel training.

Cédric Le Savéant shared this feedback with you during a webinar on Thursday, June 20th at 11:30 am: "TECHNICOLOR & ASKR.AI: GET A CLEAR VIEW OF SUPPLIER EXPENDITURES IN 2 SECONDS"! (french)

* Qualitative survey ASKR.AI, between 2019 april to 2019 may .

Setting up a DATA team : some marketing experiment feedback !

"Data is not the solution to everything: it's not a strategy in itself!" Francois Xavier Pierrel, Chief Data Officer at JCDecaux

Following the Data Marketing and Big Data World shows, here are some key points to keep in mind if you're looking to use your data on marketing projects, but don't know where to start.

TIP #1 - GET STARTED!

"Learning on the go is possible, even as a Chief Data Officer" says Francois Xavier Pierrel, Chief Data Officer at JCDecaux. It may seem basic, but if the subject interests you or members of your team, learn about it. Read articles, meet peers who have launched projects, or start from a problem you would like to solve.

The technical aspect of data storage can seem daunting, but clearing up the subject makes it easier to get started.

TIP #2 - CHOOSE YOUR USE-CASES

As stated in the beginning of this article, data is NOT a strategy. It must serve a purpose, support decision making.

What used to be the prerogative of Business Intelligence, digital transformation and Big Data has brought it to many more people in the business. Employees have to make decisions with more data at their disposal, but at the same time they have to use it more often while involving more employees.

Marketing has seen, more than any other sectors, an exponential growth in the amount of data processed and an increasingly detailed collection of it online. Giancarlo Miluccio, CDO at L'Oréal, identifies up to 26,000 micro-conversion events tracked on a single e-commerce customer journey!

During his conference at DataMarketing, Cory Chaplin (Lead Data Manager, M6) outlined 3 levels of data to guide decision making.

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ANALYTICS OR DESCRIPTIVE ANALYSIS FOR INSPIRATION

Readable, accessible self service (acmes simple and autonomous) data (SQL, DataViz, collaborative)

STATISTICAL INPUT FOR STRATEGIC DECISION MAKING

Data platform (ETL pipeline) + notebook (Jupyter, Panda, Spark...) + a data scientist working closely with the business department with great statistical expertise and good explaining skills

PREDICTIVE MACHINE LEARNING

Data scientist ML + production affinity) + ML engineer (data engineer + math skills)

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There is an assessment grid for choosing which level you will need, originally provided by Cassie Korsykov, Head of Decision Intelligence at Google.

According to C.Chaplin, it is essential for the first 2 levels to have a good knowledge of SQL in order to understand how a database is structured and to facilitate queries and data visualisation. A better understanding of this structure helps to grasp the questions that the available data can return.

For those who do not have the time to learn, or for those who want to save time once they have followed a training programme, there is the askR.ai data-assistant.

TIP #3 - SET UP YOUR DATA TEAM

You will have to find a team, because the complexity of the subject will quickly require a lot of skills.

Among the recruitment criteria, consider your team's collaborative and explaining skills. Don't forget that the use-cases you have in mind will need to be explained to the business department, who will have to approve or not of the proposed solutions!

You can start with a minimum of 3 persons. Data and digital transformation is good, but a transformation grounded in reality is better. For Simon Blaquière, Director in charge of Data and Customer Care at Generali, says "it's important to create data tools while including the business and IT department in the process".

Here are the profiles of a data team you need to hire, in this specific order!

• Decision-maker

• Data Engineer

• "Core" Analyst Experience

• Analyst Statistician

• AI/ML Engineer

• Data Scientist 1, then 2

• Data Scientist Manager

• Quality Manager

• Expert Researcher

TIP #4: GET TO KNOW THE MANAGEMENT DEPARTMENT

Your data team won't just be able to rely on you or the decision-maker to drive use-cases and get feedback on them. It's going to have to involve the management department. Both because they are the ones who will make the decisions, and they need to learn how to use these new tools, but also because they are the ones who know the company’s strategy.

Severine Marquay, AI Director at Orange, insists on the central role of operational staff in the training of machines. They will be able to offer the necessary inputs for ML, but also determine the final consistency of the data results put forward!

In short, setting up a data team involves simple steps. We hope that the feedback from these professionals will help you fine tune your strategy.

Time to chat with your data !

2020 Gartner Magic Quadrant for Business Intelligence & Analytics: 3 key elements to remember!

Gartner recently published the results of its 2020 Magic Quadrant Business Intelligence & Analytics. The analyst now defines a modern analytics & BI platform with easy-to-use functionality across the entire analytical process - from data preparation to visual navigation and insights generation - with an emphasis on the self-service and the augmented parts.

Askr.ai provides an overview of the strategic trends identified by the Gartner Magic Quadrant for Business Intelligence & Analytics 2020, and some food for thought!

1 / Cloud integration and seamless data visualisation have become the new norm.

2 / Integration with existing workflows and pre-existing environment is key. (the Looker and Salesforce cases)

3 / Trend confirmed: increased analytics boosting data platforms

1/ CLOUD INTEGRATION AND SEAMLESS DATAVIZ HAVE BECOME THE NEW NORM.

What are the differences with last year regarding the key criteria for ranking platforms?

• Good dataviz capability has become a standard.

• Companies expect support in the setup of appropriate business reporting.

• Augmented analytics are real areas of differentiation and therefore key investments for software editors, whether for Machine Learning, intelligent data preparation or the generation of insights and their explanation.

Criteria that have been widely redefined with the rise of augmented analytics

When comparing the 2019 and 2020 criteria, security has been brought to the forefront: with the advent of cloud-based solutions, companies are increasingly demanding in regards of the security of the processed data.

2 / INTEGRATION INTO EXISTING ENVIRONMENTS BECOMES ESSENTIAL

Looker

With the rise of modern BI, companies need integrated platforms that eliminate the need for multiple tools and allow for highly customised reporting. Looker, which reached the challenger category in this ranking for the first time, has launched a developer portal and now works with Slack. While the latter integration is not the only one on the market (this data-assistant is also available on Slack), it clearly marks the progression of the need for natural language support. In addition, its acquisition by Google increases both its visibility on the market, while raising questions about its future integration with the Google product suite.

Salesforce with Einstein Analytics

The major surprise of 2019 remains the acquisition of Tableau by Salesforce. It already had, with its product: Einstein Analytics, a significant competitive edge over other platforms in terms of integrated implementation in business applications. To compensate for the fact that this tool was often only coupled with Salesforce, the acquisition of Tableau is a major feat for which we are still waiting for the exact consequences on the market.

2018 Gartner Magic quadrant for Business Intelligence and Analytics. (IMAGE REMOVED )

2019 Gartner Magic quadrant for Business Intelligence and Analytics. (IMAGE REMOVED )

2020 Gartner Magic quadrant for Business Intelligence and Analytics. (IMAGE REMOVED )

3 / AUGMENTED ANALYTICS: RECOMMENDATION, SIMULATION, NATURAL LANGUAGE QUERIES

Gartner refers to augmented analytics as a metric for monitoring progress. All platforms that have maintained a clear line of development on this base have ranked higher in Gartner 2020 compared to Gartner 2019 and 2018. One example of this is Oracle, which for the first time has been positioned in the visionary category for this reason. We find, mixed together, functions of Smart discovery, Smart reporting, NLP search, AI-guided simulations... Or automatic recommendation, a feature highly appreciated by respondents (SAS or askR.ai).

The concept of augmented analytics allows Tableau to remain in the Leaders group this year, with the release of Ask Data ( cf : We tested ASK DATA) and Explain data.  The use of NLP among several players reduces the competitive edge that it offered to Thoughspot, which remains a leader but is still under pressure.

If NLP was the big trend announced for 2020, it was only the sign of a much deeper transformation of Business Intelligence towards augmented analytics.

Augmented analytics is a positive evolution of BI. The askR.ai data-assistant offers an intuitive, simple and efficient way to access data thanks to NLP!

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Chat with your SAP database ! 3 steps to building a data-assistant in an SAP environment

"We wanted to create a portal to allow users to access specific financial data without going through SAP."

Laurent Godineau - IT Manager at the SAP Skills Centre - La Poste Group

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Since its launch in 2016, the SAP HANA in-memory database has been the basis for a multitude of new services on the German software company's cloud platform. SAP has been promoting this cloud-first development method for 3 years, allowing many users of this DB to host it themselves in a private cloud. This is the case of La Poste.

ACCESSING DATA STORED IN SAP HANA: WHAT ARE THE OPPORTUNITIES?

In order to offer askR.ai's data assistant services to La Poste's Purchasing teams, Laurent Godineau, head of the SAP BI environment, worked hand in hand with the start-up. In the case where the DB is hosted in the cloud, SAP provides a way allowing for a direct connection. This is the easiest solution, but it does not always reassure companies that prefer to host their data themselves.

This is the case for La Poste, which prefers to use a gateway to connect third-party applications to the SAP environment. This method of governance allows flexible management of a complex environment, while securing a well-defined perimeter of accessible data. "Having moved to BW on SAP Hana two years ago, we wanted to create a portal to allow users to access specific financial data without going through SAP." says Laurent Godineau.

ASKR.AI ADAPTS TO ON-PREMISE ON SAP: FLEXIBILITY AND PRECISION FOR LA POSTE

The project started in May 2019 with a collaboration on the connection and network part. For La Poste, it was a question of checking the connection between the askR.ai solution hosted on the cloud and their DB hosted on a private cloud. The data-assistant team is therefore using the VPN provided by the group to secure access to the gateway. The OData protocol must then be setup to query the Business Warehouse (or SAP Data Warehouse) based on SAP Hana technology.

In July, the connection work was carried out. Purchasing data is now available in real-time for askR.ai through instant and synchronous updates. Here begins the learning process!

REAL-TIME DATA ACCESS

For Laurent Godineau, the typical constraints of a SAP environment have been overcome to reach new users with askR.ai. "People who need this data but don't want to (or don't have the time!) to navigate in SAP, including a number of managers, will now have access to procurement data in a matter of seconds through different ways: Web browser, corporate portal, Microsoft Teams...".

From now on, askR.ai has been directly connected so that data access is instantaneous. No more daily updated data-extraction, and even more efficiency !

Procurement management : 7 digital challenges to overcome in 2021

Being a purchasing director in 2020 means being aware of the growing importance of its role in terms of leading innovation in the company. Purchasing has in fact a double impact, as a performance driver that is key to maintaining and improving margins, but also in being at the forefront of innovation.

What is the impact on the strategic organisation of the purchasing department? What are the new challenges arising from the digitalisation of the purchasing department?

The increase in the volume/speed/variety of data in recent years has increased the risks related to the strategic integration of data. It is therefore up to the purchasing department to ensure that it anticipates these risks in its daily operations.

—————————————-STRATEGIC INTEGRATION OF DATA———————————————-

CHALLENGE #1 - CHECKING DATA PROPERTY

More and more data is being collected by the purchasing department throughout the supply chain. This raises the question: when sensors are installed, for packaging reception for example, does the data belong to the company that manages the sensors, the manufacturer of the sensors, the company that had the sensors installed, etc.? Agreements need to be defined prior to this in order to be able to use this information correctly and transparently, as there is a real risk linked with data sovereignty. 

CHALLENGE #2 - SCRUBBING COLLECTED DATA

Collecting data is not enough to offer new opportunities. It is necessary to clean and standardise this data, which requires new skills... The purchasing department must therefore consolidate and clean its data thanks to skills developed internally, or externally with data specialists. One of the challenges will be to automate these two structural but redundant tasks

CHALLENGE #3 - PREDICTING RISKS THROUGH IA

Another challenge will be to use this data in machine learning simulations. Different solutions will be able to provide valuable input to purchasing departments, which would otherwise be impossible to get. AI offers the possibility of predicting trends (order peaks, stock shortages, cost increases or even identifying potential risk situations with suppliers (see this article).

————————————————————-SUPPLY CHAIN  ———————-————————————-

CHALLENGE #4 - SUPPLY CHAIN OPTIMISATION THROUGH CUSTOMER DATA

The increasing amount of data available on production lines represents an incredible opportunity for process optimisation. After integrating the data from the different management tools into a common IS, traceability can be clearly improved, as well as reducing storage costs. Close collaboration and data pooling between the purchasing and logistics departments will be essential for the purchasing department to meet this challenge.

CHALLENGE #5 - TACKLING INCREASING ELECTRONICS-RELATED EXPENSES

Regarding the evolution of the type of purchases within the company, purchases of electronic equipment has boomed. Between the performance needs of employees and the programmed obsolescence of suppliers, the purchasing department will have to anticipate the importance of this type of expenditure.

———————————————-—————-SOURCING—————-———————————————-

CHALLENGE #6 - FINDING INNOVATION: THE UNEXPECTED CHALLENGE

Unexpected but logical, as purchasing is nowadays at the forefront with external suppliers. As start-ups grow in size, the relationship is changing: large/small organisations, SaaS subscription... The trend at most companies to centralise purchases is driving the purchasing department to take on this new approach. But it also puts the purchasing department in direct contact with the market and its evolutions, and can become the number one source of information on innovation in the sector for the rest of the company.

———————————————- TRANSACTIONAL —————————————————-

CHALLENGE #7 - PROCESS IMPROVEMENT

This is not new to purchasing managers. However, digitalisation brings new opportunities, especially in Source-to-Pay or supplier reference management. For instance, digitalisation allows intelligent recording of the data to be integrated, or immediate access to all the elements of the transaction.

Automated Procurement-to-Pay and expense analytics accessible through a data-assistant capable of answering questions also represent a major innovation for procurement. Real-time and accurate decision making is a huge advantage for purchasing departments!

>> Discover askR.ai, the head of procurement data-assistant <<

Head of Procurement: costs that AI will bring down.

52% of Purchasing Managers list savings as the main performance indicator for their teams * 

Even if we are increasingly looking for improved performance indicators, savings remain an essential KPI in a company's overall cost strategy. The hunt for useless expenses is the main challenge for the purchasing team, always looking for a way to improve savings in processes, negotiations, indirect purchases, etc. 

► To which extent will new technologies related to machine learning and AI impact this goal? What are the practical solutions?

AI to improve supplier risk monitoring.

Suppliers are the primary contacts for the purchasing department. It is with them that a long-term relationship of trust must be created, while at the same time taking advantage of competition and deadlines. AI will put analytical power at the service of the relationship by helping determine whether the supplier will be able to fulfil its order or not. And if there is any reason to be concerned about quality by cross-checking with external sources of information, or if there is an inconsistency between the company's CSR commitment and a change in the production line.

Thanks to AI, it is also possible to identify potential fraudulent invoices, greatly reducing the duration of a malfunction.

AI to improve supplier risk monitoring.

"AI provides between 5% to 40% additional savings through spend and supplier pool analysis.“

"AI provides between 5% to 40% additional savings through spend and supplier pool analysis.“ says Xavier Laurent, former Manutan IT Director. Using reconciliation and prediction algorithms can help buyers' decision making by securing and streamlining expenses.

How does this work? AI will help identify the most important areas for optimisation, as it is able to help classify very diverse information from unstructured data, such as PDFs for example. 

The time saved is considerable, with an additional 5% to 40% savings due to these refined analyses! 

► Discover Technicolor complete video usecase

AI for instant purchase information

It should be known that 53% of purchasing managers identify better data accessibility, coupled with analytical capabilities, as the first driver for improving their performance. Launching an AI project always involves prior work on the data: scrubbing, structuring...

The data is therefore better managed, and more accessible thanks to the instantaneous computing power of AI! At Technicolor, Cédric Le Savéant VP, Digital Office - Sourcing & Supply Chain Transformation has set up a data-assistant in the Purchasing department to answer questions from operational staff about supplier expenditure. For him, the interest was " the immediate, precise and simple nature of a 24/7 response”.

Based on a part of AI called NLP, (Natural Language Processing) this assistant generates complex queries on the go that would take hours for teams. 

Many opportunities exist: immediacy, analysis capacity, risk prediction... Now it is time to see if the companies specialised on the purchasing software market will quickly integrate this kind of innovation into their product or if new competitors will come and change the rules!

A data-assistant to query Snowflake

Frustrated by the sluggishness of the major database providers they were working for at the time, 3 frenchmen funded Snowflake in 2013. Snowflake is the first cloud-native data warehouse. The team has raised $923 million since its foundation, for a current valuation of $3.5 billion. Thanks to a unique architecture, they are disrupting cloud standards, fostering innovations such as askR.ai.

Separating data processing from storage, a brilliant idea.

How did the company manage to win over competitors such as Amazon or Microsoft in just 6 years? Thibault Ceyrolle, president of Snowflake Europe, has an explanation “The cloud architecture has been designed to multiply the number of simultaneous users and the number of requests tenfold without compromising performance".

How? By separating storage from processing, i.e. by mobilising server resources on the go to respond to specific requests. Data is transferred only once, and the power of the servers is totally modular. This pay-per-use model, invoiced per second, is better suited to the needs of companies that are increasingly demanding on the scalability and cost control offered by cloud solutions.

>> Read the article "Cloud solutions are more secure than on-premise" <<

"No time to data" : a leitmotiv as important to Snowflake as it is to the data-assistant askR.ai

Snowflake's architecture significantly improves performance by reducing the time needed for queries. According to Gartner, only 35% of employees use traditional BI due to cumbersome setups and poor user experience. askR.ai and Snowflake, both are extremely simple to integrate with existing infrastructures, reducing setup time and increasing request speed. Snowflake is at the top of the data value chain, mainly used by data scientists who are able to grasp its full potential. askR.ai is further downstream, mainly used by employees who are not very familiar with the complexities of data architecture.

For Matthieu Chabeaud, CEO of askR.ai, the integration with Snowflake was obvious. "It is a new disruptive player in the Cloud industry. Companies interested in it can consider, as part of their global strategic vision, integrating a data-assistant into the daily lives of their operational staff. Snowflake is a multicloud solution, a strategy shared by askR.ai as it is also compatible with Microsoft Azure, Google Big Query and AWS. 

To make it short, these two solutions appear as essential as they complete each other when scoping the current data market innovations.

Big Data : how to lead your teams into going further with data ?

The common denominator of these meetings? Leading businesses further in the use of their data towards a "data-driven mindset".

SHARING DATA AND DEBUNKING MYTHS ABOUT AI

Cédric Le Saveant - VP Digital Sourcing Technicolor has setup Jim, a data-bot to handle purchasing data.

For Cédric Le Saveant, VP Digital Sourcing, teams need to be able to access data at all levels of the company. For almost 4 years now, he has been introducing a real change in Technicolor's purchasing culture. For him, it's very clear: having beautiful dashboards is very good, but having a tool as interactive as a bot capable of answering your questions about data is a concrete change. "It's the data that comes to you, and it changes everything! ", says the one who wants to make office tools as simple as the ones we use in our daily routine. Indeed, one of the biggest challenges when you want to put data at the heart of an organisation is the adoption rate of the solution, very much tied to its ease of use. William Marcy, Technical BI Director at TVH Consulting, says from experience: the same solution, presented with a more attractive portal, can be successful after a failure. He therefore distinguishes between two types of BIs: BI for experts, and BI for management teams. "We have very advanced but also very complex platforms that only correspond to the needs of the first category," adds Matthieu Chabeaud, CEO of askR.ai data-assistant. "However, it is the management BI teams who need to be involved in this project”.

"We must separate BI for management teams with the one for experts, and acknowledge that very complex platforms only meet the needs of experts." Matthieu Chabeaud, CEO askR.ai

Bringing natural speech to Business Intelligence is, in the end, a return to the most basic form of communication! AI technologies that allow us to communicate and get information in this way only bring back a basic and essential method of expression," reminds Matthieu Chabeaud. This is why it is particularly important to make users understand that AI is not a black box that acts like a human with unknown objectives, but rather as algorithms that can learn. For Thomas Binant of Géotrend, it is therefore necessary to give teams the means to learn and understand how to use AI tools.

FINDING INNOVATION STARTS WITH LEARNING HOW TO USE ITS DATA.

Salons Solutions Big Data - Leading businesses further in their data use.

Another challenge in adopting new tools is having IT/data teams too much in the operational side of things that they may neglect to look for new tools that can make their work easier. There are two ways of looking for innovation: either by looking for solutions themselves, or by getting support from a consulting firm. When you are an SME anchored in everyday life, you can dangerously get used to the discomfort of a solution that "does the job". Matthieu Chabeaud calls it the “fakir” syndrome, or how one no longer even feels the pain in a situation to which one has become accustomed!

It is essential to onboard BI tools in consultation with the IT department. If the IT department is excluded from the process, it is perhaps due to a lack of understanding of the pains, but also because management teams are thirsty for quick results. The IT department is not always in the loop on new decisions, but not including it from the start can significantly slow down projects. Rather, the IT department should be allowed to "restore its image" as it can bring real value to the business department. It's exactly what happened at Renault, where the project of an internal portal using a data-assistant (literally) triggered applause on its launch! "That was to tell you how great it matched between the issue and the new tool suggested for marketing data!" remembers Matthieu Chabeaud.

However, Xavier Bouteiller from Datasulting points out, that the success of data projects is based on the company's strategy: marketing, finance, information systems or sometimes the management itself. Whatever the case, it is necessary to ensure that users are as responsible as possible, by making sure that producers are also consumers. Users that regularly use the available data - whether it is to get accurate information on a contract or overall performance management over several years - are better able to see the benefits of AI solutions. The use of data opens up a path of global thinking: how can I make predictions in order to improve my decision making? Could certain tasks be automated with Machine Learning?

Autonomy and responsibility are therefore the two essential elements to lead users to new opportunities.

GO FROM SIMPLE TO ADVANCED ANALYTICS THANKS TO ASKR.AI'S DATA-ASSISTANT DISCOVER ASKR.AI

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Conference « Value creation from data : nothing compares to BI, AI and data scien sinergy. What new gen plateforms are up to the challenge ?

with Pascal Minguet - Independant journalist

Salons Solutions talIntervenants :

• William MARCY, Directeur Technique BI, TVH CONSULTING

• Thomas BINANT, CEO, GEOTREND

• Matthieu CHABEAUD, Co-fondateur & CEO, ASKR.AI

• Cédric LE SAVÉANT, VP, Digital Office - Sourcing & Supply Chain Transformation , TECHNICOLOR

Table Ronde « Popularize analytics within SMBs : which methods and tools to make business users want to go further with self-service data ? “

with Pascal Caillerez - Journalist
Intervenants :

• Matthieu CHABEAUD, Co-fondateur & CEO, ASKR.AI

• Xavier BOUTEILLER, Directeur associé, DATASULTING

Data & Innovation : listen to the business users! #Datalab

Key accounts‘ opinion on DataLab (ENGIE, TF1, MAIF…)

Data Lab, Data Fab, Data Innovation Lab… Whatever expression is used, this kind of structure have been flourishing for 4 or 5 years. Paradoxically, Gartner still pointed out in 2017 that 85% of Big Data projects failed*. Wait, what ? A datalab would not be enough to properly gather and exploit data to make it available to every business users…? Read these major accounts testimonials to find out.

DATA projects : common difficulties.

One of the main reason for failure concerning data is the low adoption rate of new tools. The issue of adopting innovation is nothing new for change managers, who are confronted to change resistance on a daily basis. It is ridiculously obvious, and yet, leading countless initiatives to death.

When it comes to enterprise data, you will then be confronted to silos. Every business unit will go right to familiar context without a glance for general value creation. And when it comes to searching for relevant use cases, you can easily be blinded by the competition showing-off instead of focusing on your own business, because you are afraid of being left aside.

These 3 dead-end have in common the neglect of the innovation concept in its core definition: searching for a new, non-existing path. The best way to avoid these dead-ends? Taking into account data-specific technological needs and offering an environment favourable to new ideas to flourish.

Definition of a datalab: innovative workspace to explore, find and execute data driven improvements of business processes.

Different structures and one goal to bind them all: transversal transformation.

A datalab has to come within the scope of a company’s global transformation. It might be its spearhead or a simple side-project. Olivier Baes, MAIF dataLab manager, presents “the MAIF digital transformation support at every step through conferences, community management and internal communication” as its datalab main mission.

Various models are possible.

An independant datalab is possible, made of data specialists working on projects as an independent cell. At Generali France for instance, the board chooses projects to implement. Employees are trained from 3 to 6 months within the datalab, according to subjects. When they are sent back to their teams, they acquired new data skills they can use and share to the whole team, explains Hélène N'Diaye, member of Generali COMEX.

At GRTgaz, it is more about experimentation offered by its datalab structure. A place outside of the company premises was chosen on purpose to give a unique space for considering submitted business issues in a different way. According to Frederic Mours, head of Datalab of the industrial leader, accessing data is a strategic challenge as most of the teams are on the field.

On the other side, Swiss Life launched a more transversal organisation with its team members. DataLab members keep their original missions and work simultaneously on AI and data science matters. The Datalab depends on the COMEX, data strategy being directed by Cynthia Traoré. The SwissLife DataLab members meet regularly to exchange on various data issues.

At TF1, data issues are handled inside of the innovation center named MediaLab. Every 6-month start-up batches answer different issues of this TV channel core business. Every Business units own a start-up with whom a project has already been set. “ The first stake is always operational, which means data is always at the center of our business concerns”, underlines Florence Caghassi, Open Innovation Program Manager at TF1.

To conclude, a structure entirely dedicated to data promotion has to be a pilar of innovation strategies of big accounts. It can be a place, a team, transversal skills … But it has to help new solutions flourish, thanks to data-based exchanges .

“Not only is data exploitation about digital, but also a mindset that comes from more team interactions. “

Nadège Vignol, Head of Data Innovation Lab Engie

* source Nick Heudecker, Gartner 2017

Sources : Le datalab de Generali France // Faut-il un data lab pour innover ? Data Analytics post // Un data lab pour l’innovation, entretien avec Nadege Vignol

Data & Innovation : listen to the business users!

CIO-France Online Conference : Joining forces on Data democratization

Focus on Renault testimonial

Some feedback on the collaboration between Renault and the start-up askR.ai. >> original article (in french - Front commun sur la data : retour d’expérience de Renault <<

Top/down strategy request appropriate intermediaries

Since 2014, Renault has clearly announced its digital transformation ambition. A corporate vision that matches the “top/down strategy” definition of a company strategic orientation pushed from the top. The following years saw the creation of a new entity called “Renault Digital” to internally develop apps and softwares to answer business units’ needs. In this transformation process, data exploitation is key.

Jean Dumas, BtoB Customer Data Manager at Renault, started working on data issues in 2015. This is the reason behind his participation in the CIO France conference “Joining forces on data democratization”. As a CRM project manager, his experience feedback is all the more relevant as he is now responsible for making these positions more "data autonomous".

Choosing a manager from the field, in order to find new data tools and to make sure they are adopted, enabled a down-to-earth approach. It is about overcoming daily issues that face Renault distributors and that Jean Dumas has also faced.

Accepting feedback (or the absence of feedback)

Jean Dumas closely monitors the implementation of a new data portal for his teams from 2015: Fleetb@se, a dashboard helping operational teams accessing key information on their local market.  This tool was a first response to business users needs.

 However, our head of project quickly became aware that this new tool was not used enough.  He started looking for a solution that would make users more autonomous without entirely rebuilding an already operational system. It only requires a simple, instantaneous access that can be used by different types of users: business users tend to give up on a tool that is not fast enough.

Jean Dumas turned to askR.ai in 2017 to add our data bot to the portal: users could ask their questions and receive an answer instantaneously, without having to dig for the answer in FleetB@se. The number of connexions were multiplied by 7 in a few months !

Thanks to its relevance, the intuitive solution sparked off a “wow effect”. After all, every car dealer’s dream was simply to get a precise and instant answer to a question they had in mind when opening FleetB@se… Question that can now be asked to askR.ai, 24/7.

Develop data exploitation with a double approach

This CIO-Online conference testimonial proved that it is complex to offer business users a solution that meets both their daily issues and the company strategic orientation. When talking about data and users, different needs combine with multiple approaches and numerous tools. Last but not least, every piece of data has a different value according to its accuracy and its timing.

Decision making comes from data, which makes easy access to data for business users paramount. That is why IT and business teams have a common stake: IT is in charge of framing technical resources, and business teams, the only ones able to decide on their true needs. This, even within the framework of a top-down transformation strategy.

Finalist to ICC START UP AWARDS at EDHEC!

Our team is proud to be a finalist of the ICC Start up Awards that will take place on November 29th in Lille.

Every start-up will in pitch in front of a specialized jury to prove he is the best innovation in retail this year . See you there !

Knowing more about ICC : https://www.connected-day.com/start-up-nominees/

Link to a more detailed french article : https://fr.askr.ai/blog-askr-francais/icc-start-ups-awards-2018-askrai-finale