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Data Governance Automation in 2018 — Think Global, Act Local

November 7, 2017

Data Governance Automation in 2018 — Think Global, Act Local

By Eric Dumain of ObservePoint

Slide 1:

Good afternoon everyone and welcome to the data governance information session. I am Eric Dumain, VP of Product at ObservePoint. Today, I will make an introduction about how to match the new challenges our digital analytics industry will be facing in 2018.

Slide 2:

Where are we coming from?

Slide 3:

Remember, it was yesterday, 1997, we had serious digital analytics starting with Omniture. To get connected to the internet, you only had this and you could enjoy this kind of strange music. About music, here is what we used at the time. And a mobile phone was just a mobile phone and nothing else. Oh, and by the way, Google didn’t even exist. In fact, our yesterday was 20 years ago. It was just last century.

Slide 4:

In 20 years, internet and mobile pipeline multiplied by 200. The number of MarTech multiplied by 100. And online generated data multiplied by 1,000.

Slide 5:

As a consequence, terabytes of data are collected every day for nothing while most relevant data points are ignored. Finding insightful data in such a garbage is almost impossible. Big Data and predictive projects are getting lengthy because of longer cleaning processes. This leads to millions of hours of work for nothing, inappropriate decisions, and loss in business opportunities. Last but not least, unnecessary data storage leads to throwing money away for nothing and to a planetary energetic disaster.

Slide 6:

Our first lesson today is that we are producing a lot of garbage data. This doesn’t help decision making, while digital analytics are everything about decisions making. It’s a bit like in the middle of the 90s when we were collecting so many data for business intelligence that we were always say, “Garbage in, garbage out.” Today, it looks like nothing has changed with digital analytics.

Slide 7:

So where are we now?

Slide 8:

Well, as an example, a data scientist is struggling to clean the data he needs every day. He spends about 90 percent of his time cleaning 60 to 80 percent of useless data.

Slide 9:

One of the main reasons is that, unlike ERP, BI, or CRM, web analytics were born without any business process reengineering model since the low volume of data generated in 1997 didn’t justify it. For instance, companies like Business Objects, Siebel, or Oracle or SAP, anticipated the growth of data for the next decade and they came with business process reengineering. This never happened with digital analytics technology vendors.

Slide 10:

Today, despite the explosion of data, the absence of business processes in digital analytics technologies leads to poverty of data and poor data quality. As a consequence, tag and data compliance are one of the most painful and recurring tasks in the whole digital industry.

Slide 11:

While digital analytics are key to achieve many, many tasks such as collecting visitor data, managing multi-channel, understanding visitor behavior, or optimizing personalization, and many others. It still remains almost impossible identify appropriate data, to convert them into insights, or to make sure we collect most insightful data. It looks like today, even though we are managing a lot of marketing technologies including digital analytics, we still make our decisions based upon a crystal ball.

Slide 12:

Our second lesson today is that digital analytics require serious business processes. To make it work it mandatory to get the involvement of multiple contributors. Digital analysts, of course, and mainly their internal customers. Such as business managers as well as digital project managers. This is key to be the global strategies that take into consideration business original specificities in the process to product relevant, accurate data.

Slide 13:

This is one of our major new challenges.

Slide 14:

Actually, digitization changes everything. Or almost. The digital transformation is at the 21st century, what the industrial revolution was at the 20th century.

Slide 15:

And digitization leads us to so-called real-time management. To highlight this, just think about a simple or complex organization. It’s looking to management an end-to-end business model since everything is becoming digital. So even though your company does not manage multiple brands, does not manage multiple countries, it has to deal with web, with mobile web, with apps, with social networks, and maybe many other digital components that make it a global organization.

This leads to the digitization of organization and of business models falling into digital ecosystems, and leading us to real-time management. To manage these whole digitization models, a newcomer arrived in our arena.

Slide 16:

And this is our Chief Digital Officer. He’s the one leading the digitization process. His range of responsibilities is so wide that he has no time to focus on tags and technologies, or even road data. The CDO is the kind of profile who eats proven insights at breakfast, you know? No need to say that delivering such insights will require highly qualified data. Just to highlight a few of his responsibilities, he’s in charge of digitizing the business model and the business ecosystem. He has a big role in changing the culture of teams, and of people. This goes down to the legal part of the tasks. Especially with the GDPR that will happen next year as of May.

Slide 17:

Since our organizations are flattened and decision processes are accelerated, we have to consider real-time very seriously. To highlight it, just think that a big data project is managed across several months. A BI project could be managed, let’s say, a couple of months. A CRM project, we are talking about weeks. But with digital analytics, it’s a different story. We are talking about days, if not about minutes.

So what we have to do is to manage about 100 percent reliable and actionable data 24/7. And this, we have to do it for any environment, web, mobile, apps, whatever, in real-time to make decisions within minutes. To achieve this, we have to deploy efficient and flexible tagging to maintain up-to-date, 24/7, in real-time. And to make it compliant with any change in our digital ecosystem. Should it be content, campaigns, releases, whatever happens with all the influencer/followers, and no to mention any kind of promotion that changes every minutes, like for Black Friday, for instance.

Working this way, we will be able to match Forrester’s 6 components for sophistication in analytics. Strategy, organization, data, analytics and metrics, process, and finally, technology.

Slide 18:

This is one of the job descriptions of a digital analytics changes significantly. You know this guy, the digital analyst, the one who loves huge spreadsheets with hundreds of rows and hundreds of columns, if not thousands. He was before focused on retargeting social networks, influencer/followers, paid search, whatever. Today, he has to deal with business strategy, with organization, with process management, and with all the tasks he had to do before.

 

What changes is that he has to do that for multiple environments and for very complex environments. He has to think global because the whole thing is global. It can’t be specific anymore to a part of an organization, or to a part of the company, or to a local geographic. The digital analyst who will think in spreadsheet and will think in local, just forget it, because this is game over.

Slide 19:

Our number three lesson today is that rules are changing. It will be about adaptability and comfort. Things are changing very quickly. To match our goals, we will have to leave our comfort bubble, our comfort zone. And to adapt ourselves very quickly to those new business goals.

Slide 20:

A data governance model will be critical to meet our challenges. We are all looking for excellence. We are all looking to building very relevant and accurate and insightful reports. We are all looking to create a very powerful PRM, VRM, whatever you call it, for profiling visitors, prospects, and customers. This is all we want to do.

Slide 21:

However, many obstacles prevent us from reaching it. However, the digital analytics fragmented model can undo it since it will never match a global strategy. Remember that in our industry, global applies to digital ecosystem complexity. Again, even if your company is not global from a market coverage perspective, or a geographic perspective, it’s global from a digital prospective. Web, mobile web, apps, third-party tags, and cookies, or social.

 

A new model will be based upon a solid company strategy and standardized metrics. Insight and anticipation will be critical to integrate business original specificities. Always remember, fragmentation is the enemy of coherence. What you share will always be bigger than what you own.

Slide 22:

As a consequence, the data collection process must be defined before any other operation. I can some of you saying, “Hey, we hear that, we talk about that for many years. And we hear about that for many years.” But anyway, bear in mind, that in 2018, this will be mandatory by GDPR privacy by design groups. Anyway data collection process will be defined before any other operation.

 

Thanks to the GDPR, we will be there very soon. This will help us matching a company or a group or an ecosystem global policy. This will secure consistence and generate applicable insights. This will give freedom for insightful specificities. We’ll be able to build sustainable tagging plans and to manage data layer more efficiently.

Five more reasons. We will secure easy and fast tagging maintenance. We’ll be able to automate control-processes for data collection, to effectively monitor data collection optimization, to meet real-time business goals, and to match GDPR guidance about privacy by design.

Slide 23:

To make it work, we will have to think global, to involve new contributors, and to act local. Think global, it’s easy. It’s one strategy, and many processes. We will have to think leadership, then to think countries, then to think brands, then to think product categories. Or for some companies, sub-domains, for instance.

We will involve one central SWAT team. This one central SWAT team will work on the one strategy, and on the many processes. We will involve one captain. We can’t have tens of captains. Everyone in the digital analytics industry want to be a captain. While he can be a contributor, if we have tens of people making decisions, we have no decision made at the end of the day. So we need one captain. This is the think leadership thing.

We will involve internal clients and local teams, internal clients and vertical teams, and finally, product teams and users.

Finally, we will be able to act local. We will be able to qualify and include specificities in our strategy processes managed by our central SWAT team. We’ll be able to benchmark and share, to industrialize and deploy, to audit, analyze and fix, and to produce insights. You can read this slide in vertical lines or horizontal as well. It works.

Slide 24:

Our fourth lesson today is about vision and teamwork. I will even say act as one. This is a key to set data governance and insight production at the heart of your strategy.

Slide 25:

So you’re thinking, “Wow! A lot of things to do. A lot of things to manage. I have a lot of work to do already, how can manage all those things?” Well, I have some good news for you. Since ObservePoint works on data governance models and automation for almost 10 years.

Slide 26:

Since getting rid of complexities, one of our goals is doing our best effort to make our solution accessible to the widest range of users. For instance, online business managers can identify inside user journeys, insightful data points they really need. And apply their own business rules without any special skills in digital analytics.

Slide 27:

ObservePoint provides a data governance automation model: ABC. A: you start by thinking and building. B: audit and discover. C: understand and manage.

How does it work? For instance, as you process, digital analytics will provide your more flexibility in production process. Business-based rules will provide you with insightful data collection. Intuitive user-journey recorder will help identify most relevant data points. Automated tag compliance audit and monitoring will secure goals compliance. Targeted assessment will move forward from probabilistic to deterministic data. Real-time alerts on changes. Changes like content changes, like campaigns, like pricing, like anything related to changes, will accelerate deployment and maintenance. Instant information sharing will lead you to higher focus on key analytics tasks and marketing actions.

Slide 28:

I have gathered a few screenshots for you to see that the user interface is very easy to understand. You have some interfaces dedicated to the “think and build” phase of the process.

Slide 29:

You have some tools dedicated to audit and discover.

Slide 30:

You have a section to understand and manage your data governance. I won’t go further into details about the user interface of the ObservePoint platform. This is not a training. If you wish to have more details, or engineering team will be more than happy to provide you some insights on what the platform can do for you.

Slide 31:

Our fifth lesson is about the way you can easily build paths of success by extensively using the ObservePoint APP. Solution. APP. Gives you any required process and tool to make your life easier as digital analysts, and to involve new contributors to succeed in meeting new digital challenges. Eventually, it will get you to be more ambitious with your digital analytics, and to address new business opportunities.

Slide 32:

We at ObservePoint are already visioning and exploring new ways to make your digital business processes and decisions faster and more relevant.

Slide 33:

To highlight this as an examples, we are always working on ongoing improvements. More horsepower under the hood, for instance. New smart features such as optimization recommendations engine, GDPR alerts, and of course, always extended MarTech coverage.

We are working on agile systems. That will be comprised of a digital analytics task manager, a dedicated environment, and an extended alert management. Working on a more structuring model with digital analytics project manager dedicated to long-term project and deployment. Integrated tagging plan management, and a full GDPR integration.

Finally, we are working on intelligence and insights. With features like KPI conversion, automated user journey build and management, automated tagging plan build and management, and some smart recommendation and insights engine

Slide 34:

Our sixth and last lesson today is about ecosystem and delivery. ObservePoint is proud to work tightly with thousands of users and hundreds of customer and partners who support us in improving our solution to make it more compliant with your needs year after year.

Slide 35:

I thank you for your participation in this presentation and I strongly encourage you to subscribe to your own free website audit. I also encourage you to participate in the many exciting sessions we have today in this summit. Thank you.

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