Tag Archive | "Data"

SMX Replay: How to use data storytelling to earn top-tier media coverage

Amanda Milligan explains three factors that have earned coverage from NPR and CNN.



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Using STAT: How to Uncover Additional Value in Your Keyword Data

Posted by TheMozTeam

Changing SERP features and near-daily Google updates mean that single keyword strategies are no longer viable. Brands have a lot to keep tabs on if they want to stay visible and keep that coveted top spot on the SERP.

That’s why we asked Laura Hampton, Head of Marketing at Impressionto share some of the ways her award-winning team leverages STAT to surface all kinds of insights to make informed decisions.

Snag her expert tips on how to uncover additional value in your keyword data — including how Impression’s web team uses STAT’s API to improve client reporting, how to spot quick wins with dynamic tags, and what new projects they have up their sleeves. Take it away, Laura!

Spotting quick wins 

We all remember the traditional CTR chart. It suggests that websites ranking in position one on the SERPs can expect roughly 30 percent of the clicks available, with position two getting around 12 percent, position three seeing six percent, and so on (disclaimer: these may not be the actual numbers but, let’s face it, this formula is way outdated at this point anyway).

Today, the SERP landscape has changed, so we know that the chances of any of the above-suggested numbers being correct are minimal — especially when you consider the influence of elements like featured snippets on click-through rates.

But the practical reality remains that if you can improve your ranking position, it’s highly likely you’ll get at least some uplift in traffic for that term. This is where STAT’s dynamic tags can really help. Dynamic tags are a special kind of tag that automatically populates keywords based on changeable filter criteria.

We like to set up dynamic tags based on ranking position. We use this to flag keywords which are sitting just outside of the top three, top five, or top 10 positions. Layer into this some form of traffic benchmark, and you can easily uncover keywords with decent traffic potential that just need an extra bit of work to tip them into a better position.

Chasing position zero with featured snippets and PAAs 

There’s been a lot of chat in our industry about the growing prevalence of SERP features like featured snippets and “People also ask” (PAA) boxes. In fact, STAT has been instrumental in leading much of the research into the influence of these two SERP features on brand visibility and CTRs.

If your strategy includes a hunt for the coveted position zero, you’re in luck. We like to use STAT’s dynamic tagging feature to monitor the keywords that result in featured snippets. This way, we can track keywords where our client owns the snippet and where they don’t. We can also highlight new opportunities to create optimized content and attempt to capture the spot from their competitors.

This also really helps guide our overall content strategy, since STAT is able to provide quick feedback on the type of content (and, therefore, the assumed intent) that will perform best amongst a keyword set.

Making use of data views 

Data views are one of the most fundamental elements of STAT. They are tools that allow you to organize your data in ways that are meaningful to you. Holding multiple keyword segments (tags) and producing aggregate metrics, they make it possible for us to dissect keyword information and then implement strategically driven decisions.

For us at Impression, data views are essential. They reflect the tactical aspirations of the client. While you could create a single templated dashboard for all your clients with the same data views, our strategists will often set up data views that mirror the way each client and account work.

Even if we’re not yet actively working on a keyword set, we usually create data views to enable us to quickly spot opportunities and report back on the strategic progression.

Here are just some of the data views we’ve grouped our keyword segments into:

The conversion funnel

Segmenting keywords into the stages of the conversion funnel is a fairly common strategy for search marketers — it makes it possible to focus in on and prioritize higher intent queries and then extrapolate out.

Many of our data views are set up to monitor keywords tagged as “conversion,” “education,” and “awareness.”

Client goals

Because we believe successful search marketing is only possible when it integrates with wider business goals, we like to spend time getting to know our clients’ audiences, as well as their specific niches and characteristics.

This way, we can split our keywords into those which reflect the segments that our clients wish to target. For example, in some cases, this is based on sectors, such as our telecommunications client who targets audiences in finance, marketing, IT, and general business. In others, it’s based on locations, in which case we’ll leverage STAT’s location capabilities to track the visibility of our clients to different locales.

Services and/or categories

For those clients who sell online — whether it’s products or services — data views are a great way to track their visibility within each service area or product category.

Our own dashboard (for Impression) uses this approach to split out our service-based keywords, so our data view is marked “Services” and the tags we track within are “SEO,” “PPC,” “web,” and so on. For one of our fashion clients, the data view relates to product categories, where the tracked tags include “footwear,” “accessories,” and “dresses.”

At-a-glance health monitoring

A relatively new feature in STAT allows us to see the performance of tags compared to one another: the Tags tab.

Because we use data views and tags a lot, this has been a neat addition for us. The ability to quickly view those tags and how the keywords within are progressing is immensely valuable.

Let’s use an example from above. For Impression’s own keyword set, one data view contains tags that represent different service offerings. When we click on that data view and choose “Tags” in the tabbed options, we can see how well each service area is performing in terms of its visibility online.

This means we can get very quick strategic insights that say our ranking positions for SEO are consistently pretty awesome, while those around CRO (which we are arguably less well known for), tend to fluctuate more. We can also make a quick comparison between them thanks to the layout of the tab.

Identifying keyword cannibalization risk through duplicate landing pages 

While we certainly don’t subscribe to any notion of a content cannibalization penalty per se, we do believe that having multiple landing pages for one keyword or keyword set is problematic.

That’s where STAT can help. We simply filter the keywords table to show a given landing page and we’re able to track instances where it’s ranking for multiple keywords.

By exporting that information, we can then compare the best and worst ranking URLs. We can also highlight where the ranking URL for a single keyword has changed, signaling internal conflict and, therefore, an opportunity to streamline and improve.

Monitoring the competitive landscape 

No search strategy is complete without an understanding of the wider search landscape. Specifically, this means keeping track of your and/or your client’s rankings when compared to others ranking around them.

We like to use STAT’s Competitive Landscape tab to view this information for a specific data view, or across the whole account. In particular, the Share of Voice: Current Leaders board tells us very quickly who we’re up against for a keyword set.

This leads to insights such as the competitiveness of the keyword set, which makes it easier to set client expectations. It also surfaces relevance of the keywords tracked, where, if the share of voice is going to brands that aren’t your own, it may indicate the keywords you’re targeting are not that relevant to your own audience.

You can also take a look at the Share of Voice: Top 10 Trending to see where competitors are increasing or decreasing their visibility. This can be indicative of changes on the SERPs for that industry, or in the industry as a whole.

Creating a custom connector for GDS 

Reporting is a fundamental part of agency life. Our clients appreciate formalized insights into campaign progression (on top of regular communications throughout the month, of course) and one of our main challenges in growing our agency lies in identifying the best way to display reports.

We’ll be honest here: There was a point where we had started to invest in building our own platform, with all sorts of aspirations of bespoke builds and highly branded experiences that could tie into a plethora of other UX considerations for our clients.

But at the same time, we’re also big believers that there’s no point in trying to reinvent the wheel if an appropriate solution already exists. So, we decided to use Google Data Studio (GDS) as it was released in Beta and moved onto the platform in 2017.

Of course, ranking data — while we’d all like to reserve it for internal insight to drive bigger goals — is always of interest to clients. At the time, the STAT API was publicly available, but there was no way to pull data into GDS.

That’s why we decided to put some of our own time into creating a GDS connector for STAT. Through this connector, we’re able to pull in live data to our GDS reports, which can be easily shared with our clients. It was a relatively straightforward process and, because GDS caches the data for a short amount of time, it doesn’t hammer the STAT API for every request.

Though our clients do have access to STAT (made possible through their granular user permissions), the GDS integration is a simpler way for them to see top-level stats at a glance.

We’re in the process of building pipelines through BigQuery to feed into this and facilitate date specific tracking in GDS too — keep an eye out for more info and get access to the STAT GDS connector here.

Want more? 

Ready to learn how to get cracking and tracking some more? Reach out to our rad team and request a demo to get your very own tailored walkthrough of STAT. 

If you’re attending MozCon this year, you can see the ins and outs of STAT in person — grab your ticket before they’re all gone! 

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How to Automate Keyword Ranking with STAT and Google Data Studio

Posted by TheMozTeam

This blog post was originally published on the STAT blog.


We asked SEO Analyst, Emily Christon of Ovative Group, to share how her team makes keyword rank reporting easy and digestible for all stakeholders. Read on to see how she combines the power of STAT with Google Data Studio for streamlined reporting that never fails to impress her clients.

Why Google Data Studio

Creating reports for your clients is a vital part of SEO. It’s also one of the most daunting and time-consuming tasks. Your reports need to contain all the necessary data, while also having clear visuals, providing quick wins, and being easy to understand.

At Ovative Group, we’re big advocates for reporting tools that save time and make data easier to understand. This is why we love Google Data Studio.

This reporting tool was created with the user in mind and allows for easy collaboration and sharing with teams. It’s also free, and its reporting dashboard is designed to take the complexity and stress out of visualizing data.

Don’t get us wrong. We still love our spreadsheets, but tools like Excel aren’t ideal for building interactive dashboards. They also don’t allow for easy data pulls — you have to manually add your data, which can eat up a lot of time and cause a lot of feelings.

Data Studio, however, pulls all your data into one place from multiple sources, like spreadsheets, Google Analytics accounts, and Adwords. You can then customize how all that data is viewed so you can surface quick insights.

How does this relate to keyword reporting?

Creating an actionable keyword report that is beneficial for both SEO and your stakeholders can be a challenge. Data Studio makes things a bit easier for us at Ovative in a variety of ways:

Automated data integration

Our team uses the STAT API — which can be connected to Data Studio through a little technical magic and Google Big Query — to pull in all our raw data. You can select what data points you want to be collected from the API, including rank, base rank, competitors, search volume, local information, and more.

Once your data is collected and living in Big Query, you can access it through the Data Studio interface. If you want to learn more about STAT’s API, go here.

Customization

Do you care about current rank? Rank over time? Major movers – those that changed +20 positions week over week? Or are you just after how many keywords you have ranking number one?

All of this is doable — and easy — once you’re comfortable in Data Studio. You can easily customize your reports to match your goals.

“Our team uses the STAT API — which can be connected to Data Studio through a little technical magic and Google Big Query — to pull in all our raw data.” — Emily Christon, SEO Analyst at Ovative Group

Custom dashboards make reporting and insights efficient and client-facing, transforming all that raw data into easy-to-understand metrics, which tell a more compelling story.

How to build your custom Google Data Studio 

There are a myriad of ways to leverage Google Data Studio for major insights. Here are just a few features we use to help visualize our data.

Keyword rank

This report gives you a snapshot of how many keywords you have in each ranking group and how things are trending. You can also scroll through your list of keywords to see what the traffic-driving queries are.

One cool feature of Data Studio when it comes to rank is period over period comparisons. For example, if you set the date range to the previous week, it will automatically pull week over week rank change. If you set the date range to the previous month, it pulls a month over month rank change.

At Ovative, we do weekly, monthly, and yearly keyword rank change reporting.

Keyword look-up tool

If you notice that traffic has declined in a specific keyword set, pop down to the keyword look-up tool to track rank trends over time. This view is extremely helpful — it shows the progress or decline of rank to help explain traffic variability.

Campaign or priority tracker

To support newly launched pages or priority keywords, create a separate section just for these keywords. This will make it easy for you to quickly check the performance and trends of chosen keyword sets.

What’s next? 

Google Data Studio is only as powerful as you make it.

The STAT API integration in Google Data Studio represents one page of our typical client’s reporting studio; we make sure to add in a page for top-level KPI trends, a page for Search Console keyword performance, and other relevant sources for ease of use for ourselves and the client.

Want more? 

Want to dive deeper into STAT? Got questions about our API? You can book a demo with us and get a personalized walk through. 

You can also chat with our rad team at MozCon this July 15–17 to see how you can go seriously deep with your data. Ask about our specialty API — two additional services to give you everything a 100-result SERP has to offer, and perfect if you’ve built your own connector.

Grab my MozCon ticket now!

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Early data around the Google June 2019 core update shows some winners, losers

This Google update that began rolling out on Monday seems like it was pretty big and the scary part, it isn’t done rolling out yet.



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How McDonald’s Is Using Data, Machine Learning, and AI to Accelerate Growth

“Our acquisition of Dynamic Yield has brought us a lot of excitement,” says McDonald’s CEO Steve Easterbrook. “Very simply put, in the online world when we’re shopping and we pick an item and put it into our shopping basket, any website will automatically suggest two or three things to go along with it. We’re the first business that we’re aware of that can bring that into the physical world. It’s really just taking data and machine learning and AI, all these sorts of technical capabilities.”

Steve Easterbrook, CEO of McDonald’s, discusses how the company is using technology to elevate the customer experience and accelerate growth in an interview on CNBC:

Continue To See How We Can Elevate the Customer Experience

As we’ve executed the growth plan we’ve spent the first two years, three or four years ago, turning the business around. Now we’ve had a couple of years of growth. We’re confident now that we’re beginning to identify further opportunities to further accelerate growth. That takes a little bit of research and development cost. It means you’ve got to bring some expertise into the business to help us do that. We’re still managing to effectively run the business. G&A is staying the same and we’re putting a little bit more into innovation.

We continue to see how can we help continue to elevate the experience for customers. With this pace of change in the world and with different technology and different innovations, whether it’s around food, technology, or design, we’re seeing opportunities that we think can either make the experience more fun and enjoyable or smoother for customers. If we can find that we’re going to go hard at it.

We need to continue growing. If where we are investing that money is helping drive growth across 38,000 restaurants then I think the shareholders and investors would be satisfied. We want to bring our owner-operators along with us as well. They’re investing their hard-earned dollars so that always means we got a business case. The owner-operators will want to see a return on their investment just the same as a shareholder would. We’ve got a wonderful check and balance in the system to help us make sure we spend that innovative money in the right way.

Using Data, Machine Learning, and AI to Accelerate Growth

Our acquisition of Dynamic Yield has brought us a lot of excitement. It was our first acquisition for 20 years. It was an acquisition in a way that was different from the past. It wasn’t looking at different restaurant businesses to try and expand our footprint. It’s bringing a capability, an IP and some talent, into our business that can help us accelerate the growth model. We completed the deal mid-April and within two weeks we had that technical capability in 800 drive-throughs here in the U.S. It’s a very rapid execution and implementation.

Very simply put, in the online world when we’re shopping and we pick an item and put it into our shopping basket, any website we’re on these days will automatically suggest two or three things to go along with it. People who buy that tend to like these things as well. We’re the first business that we’re aware of that can bring that into the physical world. As customers are at the menu board, maybe they’re ordering a coffee and we can suggest a dessert or they’re ordering a quarter pounder with cheese and we can suggest making that into a meal. It’s really just taking data and machine learning and AI, all these sorts of technical capabilities.

Mining All of the Data Will Improve the Business

The best benefit for customers is we’re more likely to suggest things they do want and less likely to suggest things they don’t. It’ll just be a nicer experience for the customer. But yes, for the restaurant itself, because we can put our drive-thru service lines in there, for example, the technical capability by mining all of the data will be to suggest items are easier to make at our busier times. That’ll help smooth the operation as well. The immediate result will be some ticket (increases). But frankly, if the overall experience is better customers come back more often. That’s ultimately where the success will be, driving repeat visits and getting people back more often.

Across the entire sector, traffic is tight right now and people are eating out less. They have been progressively eating out less for a number of years. Whether it’s the advent of home delivery, for example, which is something we participate in, but at the moment it’s just a little bit tight out there. It’s a fight for market share. Anyone who is getting growth, typically it’s because they’re adding new units. People are finding it hard to (increase) guest count growth. It’s something that we have stated as an ambition of ours. We think that’s a measure of the true health of the business. Last quarter, we did grow traffic and we’ve grown traffic for the last couple of years, but only modestly. We want to be stronger than that.

How McDonald’s Is Using Data, Machine Learning, and AI to Accelerate Growth

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Without Data, There is No Great AI, Says Informatica Exec

“Without data, there’s no great AI, says Amit Walia, President, Products & Marketing at Informatica. “Now that AI is really becoming pervasive and at scale, you really need to give it relevant good contextual data. We see that happening a lot in the world of enterprise. Finally, enterprise is arriving at the point where they want to use AI for B2B use cases, not just consumer use cases that we are used to. AI is a part of everything that we do in data.”

Amit Walia, President, Products & Marketing at Informatica, discusses how data is the lifeblood of the enterprise in an interview on theCUBE at Informatica World 2019:

Without Data There is No Great AI

The language that AI needs or speaks is data. Without data, there’s no great AI. This is something that we’ve known all this while, but now that AI is really becoming pervasive and at scale, you really need to give it relevant good contextual data. We see that happening a lot in the world of enterprise. Finally, enterprise is arriving at the point where they want to use AI for B2B use cases, not just consumer use cases that we are used to. AI is a part of everything that we do in data.

It has really helped to improve productivity and automate mundane tasks. There’s a massive skills gap and I think you look around the economy is fully saturated with jobs. There is still so much work to be done with more data and different data. AI is helping make some of those mundane activities become a lot easier and autonomous.

Data is Becoming a Platform of Its Own

Our data scientists have gone from heroes to superheroes. Think about it. What we are seeing in this world is that data is becoming a platform of its own. It is getting decoupled from the databases, from the applications, and from the infrastructure. To truly be able to leverage AI and build applications on top you cannot let it be siloed and be held hostage to its individual infrastructure components. We’ve seen that fundamental change happening where data as a platform is coming along.

In that context, the catalog becomes a very pivotal start because you want to get a fuller view of everything. You’re not going to be able to move all of your data to one place. It’s impossible. But understanding that metadata is where enterprises are going and then from there you can have a customer experience journey with MDM. You can also have an analytics journey in the cloud with an AWS or an Azure. You can have complete governance and security and privacy journey while understanding anomalous activity.

Metadata Is the New OS

Data is everywhere. It’s like the blood flowing through your body. You’re not going to get all the data in one place to do any kind of analytics. You’re going to let it be there. We say that metadata is the new OS. Bring the metadata, which is data about the data in one place, and from there let AI run on it. What we think about AI is this; LinkedIn is a beautiful place where they leverage the machine learning algorithm to create a social graph about you and me. If I’m connected to John I know now that I can be connected with you. The same thing can happen to the data layer.

When I’m doing analytics and I’m basically searching for some report, through that same machine learning algorithm at the catalog level now we can tell you that this is another table or another report or another user and so on. We can give you help back ratings within that environment for you to do what I call analytics on your fingertips at enterprise scale. That’s an extremely powerful use case of taking analytics, which is the most commonly done activity in an enterprise and make it accurate at enterprise scale.

Without Data, There is No Great AI, Says Informatica’s Amit Walia

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Michael Dell Predicts in 10 Years More Computed Data on the Edge Than Cloud

“The surprise outcome ten years from now is there’ll be something much bigger than the private cloud and the public cloud,” says Dell Technologies CEO Michael Dell. “It’s the edge. I actually think there will be way more computed data on the edge in ten years than any of the derivatives of cloud that we want to talk about. That’s the ten-year prediction.”

Michael Dell, Chairman and CEO of Dell Technologies, discusses how it has become a critical technology platform for its customers in an interview with theCUBE at Dell Technology World 2019 in Las Vegas:

Data Has Always Been at the Center of How the Technology Industry Works

We feel great. Our business has really grown tremendously. All the things we’ve been doing have been resonating with customers. We’ve been able to restore the origins of the entrepreneurial dream and success of the company and reintroduce innovation and risk-taking into a now $ 91 billion company growing at double digits last year. Certainly, the set of capabilities that we’ve been able to build organically and inorganically, with the set of alliances we have, the trust that customers have given us, we are super happy about the position that we’re in and the opportunities going forward. I think all this is really just a pregame show to what’s ahead for our industry and for the role that technology is going to play in the world.

Data has always been at the center of how the technology industry works. Now we just have a tsunami, an explosion of data. Of course, now we have this new computer science that allows us to reason over the data in real time and create much better results and outcomes. That combined with the computing power all organizations have to reimagine themselves given all these technologies. Certainly, the infrastructure requirements in terms of the network, the storage, that compute, the build-out on the edge, tons of new requirements, we’re super well-positioned to go address all that.

Predicts in 10 Years More Computed Data on the Edge Than Cloud

The surprise outcome ten years from now is there’ll be something much bigger than the private cloud and the public cloud. It’s the edge. I actually think there will be way more computed data on the edge in ten years than any of the derivatives of cloud that we want to talk about. That’s the ten-year prediction. That’s what I see. Maybe nobody’s predicting that just yet, but let’s come back in ten years and see what it looks like.

Really what we’re doing is we’re bringing to customers all the resources they need to operate in the hybrid multi-cloud world. First, you have to recognize that the workloads want to move around. To say that they’re all going to be here or there is in some sense missing the point because they’re going to move back and forth. You’ve got regulation, cost, security, performance, latency, all sorts of new requirements that are coming at you and they’re not going to just sit in one place.

This is All Super Important As We Enter This AI Enabled Age

Now with the VMware cloud foundation, we have the ability to move these workloads seamlessly across now essentially all the public clouds. We have 4,200 partners out there, infrastructure on-premise built and tuned specifically for the VMware platform and empowered also for the edge. All of this together is the Dell Technologies cloud. We have obviously great capabilities from our Dell UMC infrastructure solutions and all the great innovations at VMware coming together.

Inside the business, the first priority was to get each of the individual pieces working well. But then we saw that the real opportunity was in the seams and how we could more deeply integrate all the aspects of what we’re doing together. You saw that on stage you know in vivid form yesterday with Pat and Jeff and Satya and even more today. Of course, there’s more to do. There’s always more to do. We’re working on how we build a data platform bringing together all of our capabilities with Boomi and Data Protection and VMware. This is all going to be super important as we enter this AI enabled age of the future.

We’ve Created an Incredible Business

I think investors are increasingly understanding that we’ve created an incredible business here. Certainly, if we look at the additional coverage that we have as they’re understanding the business, some of the analysts are starting to say hey this doesn’t really feel like a conglomerate. It’s a direct quote. If you think about what we demonstrated today and yesterday and will demonstrate in the future we’re not like Berkshire Hathaway. This is not a railroad that owns a chain of restaurants. This is one integrated business that fits together incredibly well and it’s generating substantial cash flows.

I think investors over time are figuring out the value that’s intrinsic to the overall Dell Technologies family. We’ve got lots of ways to invest, we got VMware, SecureWorks, Pivotal, and of course the overall Dell Technologies.

Michael Dell Predicts in 10 Years More Computed Data on the Edge Than Cloud

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Google Ads store visits, store sales reporting data partially corrected

Google says it is making progress, but there are still days for which reporting is inaccurate.



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Huge Volume of IoT Data Managed via AI Creates Real Value, Says Oracle VP

“What’s interesting is that IoT has been around for a long time but as companies start to enable it and start to leverage it more and more there’s just huge volumes of data that have to be managed and be able to analyze and be able to execute from,” says John Barcus, Vice President Manufacturing Industries at Oracle. “One of the technologies that is really exciting is this whole concept of AI. It really allows you to use that information and correlate it with a lot of different pieces of information.”

John Barcus, Vice President Manufacturing Industries at Oracle, discusses how technologies such as AI and blockchain are now helping companies manage huge volumes of IoT data in an interview with technology influencer Ronald van Loon:

Companies Are Moving Toward Selling Products as a Service

I think that (manufacturers connecting all the processes digitally) is the way that will differentiate them. It’s really the only way the companies will be able to survive into the future. There are all these business models and it has become significantly more competitive than it has been in the past. Companies have to work faster and they have to be more responsive to what their customer needs are. The only way really of doing that is to connect the various aspects of the business. They can’t work in silos anymore. That really will give you the whole value of the business.

One area that companies are moving away from is selling products. They’re going into selling more services which we’ve actually seen for some time. But what they’re now getting into is these new models where they might be selling products as a service. If you think about how do you sell a product as a service and the ability to support that it is a lot different than it was before. Connecting to that product and being able to anticipate activities, anticipate needs, anticipate failures, and to be able to monitor how it’s performing, how the customers use it and are able to expand on that to be able to provide a better outcome for the customer are important components.

Huge Volume of IoT Data Managed via AI Creates Real Value

What’s interesting is that IoT has been around for a long time but as companies start to enable it and start to leverage it more and more there’s just huge volumes of data that have to be managed and be able to analyze and be able to execute from. One of the technologies that is really exciting is this whole concept of AI. It really allows you to use that information and correlate it with a lot of different pieces of information. You can correlate with the data that might be in your ERP and your MES and other sources of information and actually provide some real value and provide the real outcomes. It can now do some predictions where it would be actually physically impossible for people to do the same type of calculations that they’ve been doing in the past with this huge volume today.

The second area where there seems to be a little hesitation at the moment is around blockchain. But the technology is there and people have been trying to identify how best to use it. Some of the use cases that are coming out now are going to be quite impressive. I think the little bit of a lull was deserved. People who looked at it anticipate a little bit more than what was possible and now they’re really starting to develop some good use cases. I think there’s a lot of opportunities in that area.

Huge Volume of IoT Data Managed via AI Creates Real Value, Says Oracle VP John Barcus

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All Google Ads attribution reports will soon include cross-device conversion data

Make note, the change takes effect May 1.



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