Gartner Data & Analytics Summit 2018

March 19-21 in London, the conference was Gartner Data & Analytics Summit . I was a visitor to this event and I want to share with you my thoughts and observations.
Gartner Data & Analytics Summit 2018  
Gartner Events Navigator . First, looking at the required privileges, I did not want to install it, because I was afraid that I would not get anything of value for myself (in addition to what is already on the site), but I will provide a lot of data about myself to a third-party company. But after reading all the possibilities of the application, I installed it and as a result I was very pleasantly surprised at how much it was claimed and thought out by the authors. Judge for yourself, in the application you get:
a) Personal scheduler with the ability to watch the schedule, schedule visits to favorite presentations and download materials in PDF to phone
b) Social network and chat of participants
c) The layout of the halls
d) Feedback tools
e) A recommendatory service that will select the best presentations for a visit depending on your interests.

I've been actively using the application for all three days and I think what happened to me recently was the experts from the field of ethics of large data, who would willingly share their personal data with the application if they understand what value they receive in return.
The second thing that I would like to note is the digitization of human flows at the conference.
Each participant of the conference wore a badge with a badge, and at the entrance to each room and every demonstration booth organizers with the help of this device scanned all visitors who showed interest precisely to this topic:

Given that each participant at the registration indicated enough professional information about himself, there were approximately 1500 participants, and the event lasted 3 days, all this Internet
people generated a very interesting array of data, which, in addition to direct study, surely can also be monetized.
Plus what I liked more - every performance started and ended strictly on time, minute to minute.
Turning to the conference itself, we can not but mention supersaturated (pdf) agenda. There were several types of events: presentations, round tables and master classes and an exhibition with more than 50 vendors. It was necessary to assume in advance that all 100% of the events will not be visited, from the very beginning it is necessary to adjust to a compromise and implement a painful choice.
It is interesting for me to post-factum analyze the statistics of topics touched upon at the conference (presentations and round tables):

From my point of view, this disproportion in favor of Data Governance is somewhat artificial. It seemed to me that the true leader from the point of view of the user's interest was the theme "AI & ML", anyway, I could not get to any round table or master class on ML because the registration for them already by the beginning of the event were closed, while at events on Data Governance there were free slots.
Below I quote several theses /terms /thoughts that I remembered after the last three days of presentations and presentations:
Organizational structure - The most common hybrid model. There is a Chief Data Officer, who reports to the CEO or CIO. Data Scientists in an organization are usually of two types: with a particular business function or in a separate Center of Excellence. The purpose of the Center of Excellence is RnD and innovative tasks that can not be attributed to any of the existing businesses. CDO is responsible for coordinating Data Scientists. He also defines the goals and strategy of development.
Only 20% of the polled visitors have the role of CDO in their staff structure. Forecast Gartner - by 2020 this figure should grow to 40-50%.
Data Lake - the most common are the following implementations:
1. Hadoop
2. Object Store (for example, Amazon S3)
Data Lake is not the "killer" of Enterprise DWH, but complements it in the corporate IT landscape.
Security in Data Lake /Hadoop - this is a real risk, as for the reason, there are no standard tools unlike RDBMS, and because virtually no one invests in the implementation of security policies in Hadoop: authentication, authorization, audit.
Data Literacy (literacy with t.z. work with data) - an asset in which you must invest. Altogether, about 5% of organizations can boast of having end-to-end programs to upgrade the level of Data Literacy at all levels - from Executive Management to Junior specialists.
Logical DWH - a way to organize access to data, in which there is no centralized storage of data, such as in DWH /Data Lake. However, for users such an organization is absolutely transparent, and they can use data quite calmly, without thinking about how access is organized to them. (It turns out that there are already a lot of solutions on this topic, see below.)
Open Source - Many vendors are starting to make Community versions of their commercial products and distribute them for free, thus recognizing the consistency of such a model, and at the same time earning a loyal fan base (However, not all of them.) I had a dialogue with one vendor on how I can try their answer.The answer was that in order for them to send me the activation keys, I must write them a letter, sign the NDA, draw up a PoC contract, in which to write out the criteria for success and commitment to purchase their product if all the criteria shnosti are met. ¯_ (ツ) _ /¯)
In addition to presentations and reports that were going on in several streams, the exhibition hall in the big hall constantly operated in the center of the conference, in which it was possible to get to know the representatives of companies and see the demos of their products.
In a structured form, these are the companies that could be seen there:
Major well-known vendors:
- Oracle - represented their cloud platform
- Qlik - Self-service BI solutions
- Tableau - Self-service BI solutions
- Ataccama - Data Quality solutions, Master Data Management, Data Govenance
- Google Cloud - the cloud platform
- IBM - represented solutions for Data Science: DSX & SPSS
- Attunity - data replication solution /CDC
- Teradata
- Microstrategy
- Informatica
- Information Builders
Commercial assemblies Hadoop:
- Cloudera
- MapR
- Hortonworks
Solutions for Data Science:
- Dataiku
- Rapid Miner
- R Studio
- Angoss (
Solutions for Data Governance:
- Alation ( )
- Backoffice Associates (
? )
- Collibra ( )
- Semarchy ( )
- Stibo Systems ( )
Solutions for Self Service BI:
- Arcadia Data (
? )
- Looker ( [url]Https://[/url] )
- Sinequa (
? )
- ThoughtSpot ( [url]Https://[/url] )
Software for building a logical DWH:
- Actian ( )
- BI Builders ( ? )
- Denodo ( ? )
- Domo ( )
- Dremio ( ? )
- Iguazio ( )
- Sisense ( )
- Snowflake ( )
- Trifacta (
? )
- WhereScape ( [url]Https://[/url] )
Of course, not all the solutions that were presented, but to review them all in such a short time between the presentations is almost impossible task, especially considering that after the third or fourth company everything starts to mix in your head.
Briefly about the conclusions.
The set goals have in general been achieved. We got an idea of ​​the direction in which the market develops, what players are on it, and where we are now from the point of view of technology (Hadoop, Data Lake, Streaming) and processes (Governance, Security, staff training, etc.) summit. These conclusions will form the basis of the immediate development plans.
What surprised us most was the large number of relatively young vendors that are targeting the "Logical DWH" segment, and behind which, in general, the giants do not have time. For me, "Logical DWH" is definitely a topic in which it is necessary to understand in more detail and in-depth.
Well, in general, I confirmed for myself the conclusion that such events are useful from the point of view of broadening the horizon and understanding in which direction the progressive community is moving, so that it would be easier to understand where to develop further.
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