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Q&A with Microsoft’s Noël Reilly: Data, discovery, customer-first mindset

September 21, 2019 No Comments

Noël Reilly, Strategic Account Director at Microsoft, discusses her upcoming session at The Transformation of Search Summit, which will take place on October 25 here in New York.

As a prelude to the event, we’re doing a series of Q&As with speakers and panelists. First up, we have Noël, who will be on the panel, “Embarking on a search transformation project.”

noel rielly microsoft

Tell us a bit about your role at Microsoft? 

Noël Reilly: As a Strategic Account Director for Microsoft Advertising, it’s my responsibility to partner with our top global clients and ensure they understand our Microsoft value proposition and how to best leverage Microsoft Advertising products to reach their customers.

My main goal is to help build client relationships and partner with them on digital strategies which empower and grow their business.

What are your key priorities over the next twelve months?

NR: Our Microsoft Advertising offering is evolving quickly — we’ve come out with a ton of solutions, particularly in the audience, automation, and ecommerce space.

My first key priority is of course educating our clients on how best to utilize these tools to drive their goals.

We’re also looking at ways in which to bring a single voice from Microsoft to our clients — I’ve been partnering heavily with the enterprise side of our business to learn more about and deepen the relationship across top clients. 

What is your biggest challenge in achieving those? 

NR: Like all digital advertising, search in particular generates a ton of data, and we as marketers can get overwhelmed by it!

While all of this data we have access to has opened a ton of new possibilities for brands to respond, we also have to make sure we are respecting the people behind that data.

So much is changing in this space right now — with GDPR in Europe to now California following suit, there’s a lot out there to be knowledgeable of.

Add that to the way that search and discovery are changing with things like voice and image, and you’ve got yourself a really complex ecosystem.

Microsoft has taken a people-first approach to all of this. We sometimes get questions from marketers on when we’re coming out with this targeting or that data cut.

And while we are committed to client success, we have taken an industry-leading approach to brand safety and privacy standards and creating solutions that provide value while keeping customer data secure. 

What’s your advice to others who may be facing similar challenges?

NR: Ask questions! If you’re working with a partner who isn’t transparent about where their data comes from, or where your ads are showing up, you should ask why that is.

Automation is a hugely powerful tool for marketers. It can reduce a ton of bandwidth and be a key driver of your digital transformation journey.

Microsoft has tons of new products that can make personalization and automation easier, from product audiences which supercharge your ecommerce remarketing across our network, to automated bidding, to even our handy recommendations tab.

They all use the power of the Microsoft graph which can help you save time and work the way you want to work, but also offer that industry leading transparency and trust I mentioned earlier.

What’s an interesting trend you’re seeing in the market right now?

NR: We have a tendency to focus on the tools: voice search, targeting, and audiences — but those things themselves are not the disruptors.

It’s things like conversational AI, and the new consumer experience, which are what’s interesting.

If you think about search and what it fundamentally has always been: it’s a place to discover and get answers.

This has not changed, but the way consumers engage with it has. And I think marketers are working to figure out the best way to tap into this new ecosystem. 

How do you expect it will change in the next 6-12 months?

NR: I think the focus on designing for every customer experience is what will start to take the spotlight in the next 12 months.

We at Microsoft give you a ton of ways to tap into your consumer. For example, if you want to understand what voice search looks like for your brand, you could run a search query report and look for indicators of voice queries in the intent.

If you want to personalize your message, you can leverage responsive search ads.

But, just using the tools does not guarantee success. The brands who know their customer journeys and are investing in unifying their data are the ones who we see improving their marketing performance.

Tell us a bit about your session at the Search Summit?

NR: My goal is of course to inspire you to not be overwhelmed by all of the changes in the world of search but inspired to put it at the forefront of your marketing strategy — there’s no better way to understand the journey of your consumer.

What are you looking forward to most at the Summit?

NR: I’m obsessed with our clients — I’m really looking forward to hearing from the folks at Walt Disney, Volvo, and Conde Nast.

As someone whose biggest responsibility above all is client satisfaction, I have to know and stay in touch with what is top of mind for clients.

What’s one of your favorite search technologies and why? 

NR: You’re talking to someone who has worked in Search for the last 12 years, so I pretty much nerd out on all of them.

If I have to pick a favorite though, it would definitely be our newly created Audience Ads on the Microsoft Audience Network.

It leverages search and web activity as well as demographic and professional targeting to really allow the advertiser a ton of cool options for targeting, and we’re seeing really great success from our clients who are investing time into this product.

What’s something you do every day that helps you be more successful or productive? 

NR: I am an avid yogi and teach fitness classes outside of work, so this has always been an important part of my life.

I recently moved about an hour and a half outside of the city, so I’ve had to do a lot of adjusting on my time table.

I make sure every morning I use the commute time to answer emails and create my working agenda for the day, so by the time I arrive at the office, I’m ready to hit the ground running.

Things always come up, but I have found that rising early and taking advantage of the quietness of the morning has given me time back to still get things done I need to do while keeping my time in the evenings personal for myself, my family, or my students. 

The post Q&A with Microsoft’s Noël Reilly: Data, discovery, customer-first mindset appeared first on Search Engine Watch.

Search Engine Watch


How to improve SEO using data science

September 5, 2019 No Comments

Gone are the days when a single tweak in the content or the title tag was able to get your site to the top of the search results. 

Google algorithm is now much harder to crack than before. Besides, 75 percent of online users do not scroll past the first page of the search engine results.

As you can imagine, this makes the SEO space highly competitive right now and companies can no longer rely on basic techniques.

However, you can always make sure that the odds are in your favor by using data science.

What is data science?

A combination of various tools, algorithms, and machine learning principles designed to unveil hidden patterns using the raw data is referred to as data science.

Data science is creating its impression across every domain. As cited by Maryville University, around 1.7 megabytes of data will be generated every second for everyone on the planet by the end of 2020.

Why do you need it?

Data science provides valuable insights about a website’s performance and these insights can help you improve your SEO campaigns.

Data science is used to make predictions about upcoming trends and customer behavior using analytics and machine learning. For example, Netflix uses insight from data science to produce its original series that drives user interest.

Apart from identifying opportunities, data science also handles high voluminous data and helps in making better decisions. Businesses can easily gauge the effectiveness of a marketing campaign with the help of data science.

How does data science help SEO?

Data science helps you make concrete decisions by letting you:

  • Visualize which combinations have the potential to make the biggest impact
  • Create marketing campaigns aligned with the needs of their audience
  • Understand buyer’s preferences and identify pain points
  • Identify referral sources of converting traffic
  • Verify loading time, indexing, bounce rate, response errors, and redirects
  • Verify the most and least crawled URLs
  • Identify pages that crawlers aren’t supposed to index
  • Identify sources of unusual traffic

How do you apply data science to your SEO data?

Follow the below ways to apply data science to your SEO campaigns:

1. Select your data sources

Understand that the quality of your data sources directly impacts your data insights. You need the right tools to track important metrics more precisely. The top four tools that can help you gather the right data and make better decisions are Google Analytics, SEMrush, and Ahrefs.

2. Think “ecosystem” instead of “data” and “tools” 

Do not rely on one solution if your SEO is complex and integrates with various other digital marketing areas like content marketing, CX management, CRO, and sales. The “data science” approach to SEO is about integrating methods, tools, and practices in a way that draws deep and accurate insights from the cumulative data mix. Consider the SEMRush console we discussed above. The traffic stats it presents work on the assumption that all traffic is genuine. What if there are bad bots at play here? It makes a lot of sense to bring in a traffic quality checking tool into the mix, something like what Finteza does.

Example of using Finteza to improve SEO using data science

Source: Finteza

It offers you advanced bot detection tech, along with a whole suite of conversion funnel optimization modules, to help you not only make more sense of your data but also to put the insight into action, to drive business KPI scores.

3. Align SEO with marketing initiatives 

Backing your SEO with other marketing initiatives makes it stronger. Collaborate with sales, developers, UX designers, and customer support teams to optimize for all search ranking factors.

Use data science to determine a universal set of SEO best practices each team can follow to achieve your goal. Try tracking the evolving relationships between independent and dependent variables to get a better idea of what actions are important to your business. To fully understand how your SEO affects other channels, capture and analyze data from:

  • Top conversion paths
  • Conversions and assisted conversions

Gain a clear understanding of your customers’ journeys to establish a stronger alignment between various marketing activities and attribute the outcomes to separate campaigns easily.

4. Visualize with data science

Find it hard to digest numbers piled onto a spreadsheet? Taking a hierarchical approach to your data can cause you to miss out on important hidden between the lines. On the other hand, draw different benefits from data visualizations like:

  • Compare and contrast
  • Process large data volumes at scale
  • Accelerate knowledge discovery
  • Reveal hidden questions
  • Spot common patterns and trends

Test it out yourself. Leverage data science during an SEO technical audit and receive insights about your site’s health and performance. Use that data to know more about your page authority, rankings, number of outbound/inbound links per page, and other factors. However, you won’t find a proper answer about why some pages perform better in the search results, while the others lag behind. Visualizing the site’s internal link structure and figuring out the domain authority of individual pages on a scale of one to ten (like Google) allows you to see the areas for improvement and adopt proactive measures.

On-page SEO optimization is just a single example of how SEO experts combine visualizations with data science to provide better results to clients. Make your SEO data more actionable with visualizations.

5. Take help of A/B testing

LinkedIn carried out an experiment using the XNLT platform. The experiment was focused on the redesign of the “Premium Subscription” payment flow. The LinkedIn UX team reduced the number of payment checkout pages and added a FAQ. The results were impressive with an increase in the number of annual bookings which was worth millions of dollars, a 30% reduction in refund orders and a 10% increase in free trial subscriptions.

Concluding remarks

Data science focuses on eliminating guesswork from SEO. Rather than presuming what works and how a specific action affects your goals, use data science to know what’s bringing you the desired results and how you’re able to quantify your success. Brands like Airbnb are already doing it and so can you.

The post How to improve SEO using data science appeared first on Search Engine Watch.

Search Engine Watch


Data Studio Showdown: Dashboards vs Reports

August 30, 2019 No Comments

Do your clients need automated dashboards or narrative reporting? Learn how to create (and deliver) each using Google’s free Data Studio.

Read more at PPCHero.com
PPC Hero


The Great Hack tells us data corrupts 

July 29, 2019 No Comments

This week professor David Carroll, whose dogged search for answers to how his personal data was misused plays a focal role in The Great Hack: Netflix’s documentary tackling the Facebook-Cambridge Analytica data scandal, quipped that perhaps a follow up would be more punitive for the company than the $ 5BN FTC fine released the same day.

The documentary — which we previewed ahead of its general release Wednesday — does an impressive job of articulating for a mainstream audience the risks for individuals and society of unregulated surveillance capitalism, despite the complexities involved in the invisible data ‘supply chain’ that feeds the beast. Most obviously by trying to make these digital social emissions visible to the viewer — as mushrooming pop-ups overlaid on shots of smartphone users going about their everyday business, largely unaware of the pervasive tracking it enables.

Facebook is unlikely to be a fan of the treatment. In its own crisis PR around the Cambridge Analytica scandal it has sought to achieve the opposite effect; making it harder to join the data-dots embedded in its ad platform by seeking to deflect blame, bury key details and bore reporters and policymakers to death with reams of irrelevant detail — in the hope they might shift their attention elsewhere.

Data protection itself isn’t a topic that naturally lends itself to glamorous thriller treatment, of course. No amount of slick editing can transform the close and careful scrutiny of political committees into seat-of-the-pants viewing for anyone not already intimately familiar with the intricacies being picked over. And yet it’s exactly such thoughtful attention to detail that democracy demands. Without it we are all, to put it proverbially, screwed.

The Great Hack shows what happens when vital detail and context are cheaply ripped away at scale, via socially sticky content delivery platforms run by tech giants that never bothered to sweat the ethical detail of how their ad targeting tools could be repurposed by malign interests to sew social discord and/or manipulate voter opinion en mass.

Or indeed used by an official candidate for high office in a democratic society that lacks legal safeguards against data misuse.

But while the documentary packs in a lot over an almost two-hour span, retelling the story of Cambridge Analytica’s role in the 2016 Trump presidential election campaign; exploring links to the UK’s Brexit leave vote; and zooming out to show a little of the wider impact of social media disinformation campaigns on various elections around the world, the viewer is left with plenty of questions. Not least the ones Carroll repeats towards the end of the film: What information had Cambridge Analytica amassed on him? Where did they get it from? What did they use it for? — apparently resigning himself to never knowing. The disgraced data firm chose declaring bankruptcy and folding back into its shell vs handing over the stolen goods and its algorithmic secrets.

There’s no doubt over the other question Carroll poses early on the film — could he delete his information? The lack of control over what’s done with people’s information is the central point around which the documentary pivots. The key warning being there’s no magical cleansing fire that can purge every digitally copied personal thing that’s put out there.

And while Carroll is shown able to tap into European data rights — purely by merit of Cambridge Analytica having processed his data in the UK — to try and get answers, the lack of control holds true in the US. Here, the absence of a legal framework to protect privacy is shown as the catalyzing fuel for the ‘great hack’ — and also shown enabling the ongoing data-free-for-all that underpins almost all ad-supported, Internet-delivered services. tl;dr: Your phone doesn’t need to listen to if it’s tracking everything else you do with it.

The film’s other obsession is the breathtaking scale of the thing. One focal moment is when we hear another central character, Cambridge Analytica’s Brittany Kaiser, dispassionately recounting how data surpassed oil in value last year — as if that’s all the explanation needed for the terrible behavior on show.

“Data’s the most valuable asset on Earth,” she monotones. The staggering value of digital stuff is thus fingered as an irresistible, manipulative force also sucking in bright minds to work at data firms like Cambridge Analytica — even at the expense of their own claimed political allegiances, in the conflicted case of Kaiser.

If knowledge is power and power corrupts, the construction can be refined further to ‘data corrupts’, is the suggestion.

The filmmakers linger long on Kaiser which can seem to humanize her — as they show what appear vulnerable or intimate moments. Yet they do this without ever entirely getting under her skin or allowing her role in the scandal to be fully resolved.

She’s often allowed to tell her narrative from behind dark glasses and a hat — which has the opposite effect on how we’re invited to perceive her. Questions about her motivations are never far away. It’s a human mystery linked to Cambridge Analytica’s money-minting algorithmic blackbox.

Nor is there any attempt by the filmmakers to mine Kaiser for answers themselves. It’s a documentary that spotlights mysteries and leaves questions hanging up there intact. From a journalist perspective that’s an inevitable frustration. Even as the story itself is much bigger than any one of its constituent parts.

It’s hard to imagine how Netflix could commission a straight up sequel to The Great Hack, given its central framing of Carroll’s data quest being combined with key moments of the Cambridge Analytica scandal. Large chunks of the film are comprised from capturing scrutiny and reactions to the story unfolding in real-time.

But in displaying the ruthlessly transactional underpinnings of social platforms where the world’s smartphone users go to kill time, unwittingly trading away their agency in the process, Netflix has really just begun to open up the defining story of our time.


Social – TechCrunch


Guide to call tracking and the power of AI for analyzing phone data

July 27, 2019 No Comments

Invoca, an AI-powered call tracking platform, published their Call Tracking Study Guide in March of this year. The in-depth guide demystifies call tracking technology and reviews how call tracking tools help marketers connect digital campaign data to inbound customer phone calls.

Call tracking is a powerful way for marketers to understand exactly where phone calls are coming from with granularity that, for the most robust tools, can extend down to the keyword level. This data helps reveal what platforms, publishers, keywords, and channels drive high-intent customers to call and can help marketers create a more informed media allocation strategy. 

Content produced in collaboration with Invoca.

Call tracking 101: A brief introduction

Invoca uses a snippet of JavaScript code placed on your website to track calls. After the code snippet is placed on the landing page, it swaps out your standard business phone number with a trackable, dynamic phone number which is unique to each website visitor. 

The tag also captures various referrer elements such as utm source, medium, paid search keyword and Google click ID—this is what enables Invoca to connect user data to phone calls.

Example of dynamic tracking phone numbers on a landing page

Example of dynamic tracking phone numbers on a landing page—source: Invoca

When the tracking number is called, the platform can also route the caller to the appropriate person or call center depending on what marketing content they are viewing, reducing time on hold and call transfers. Data is collected based on the specific call number which can include caller information, keyword, referrer type (e.g., banner ad, search ad, or social media ad) and referral source (e.g., Google, Facebook, etc.) which can also be used to inform the call center and create a highly personalized experience for the caller.

Example of referral data info in Invoca

Example of referral data info in Invoca

Not all call tracking tools are created equal

There is a large selection of call tracking tools on the market that range from basic to advanced in terms of features and functionality. 

Basic tools provide limited data to marketers, but they ignore the larger customer journey and tend to focus on last-touch attribution (e.g., making it difficult or impossible to determine where the call came from).

Some metrics a basic tool might track include:

  • Call volume
  • Call time and duration
  • Caller information
  • Basic campaign attribution

These tools provide some sense of campaign performance, but fail to tell the full story that can be gleaned when connecting analytics platforms (e.g. Google Analytics) to call information. 

More advanced AI-powered call tracking tools like Invoca aim to bridge that gap, while also automating some marketing actions after the call takes place. 

Advanced capabilities that AI-powered call tracking tools provide include:

  • Touchpoint attribution—Tie a call back to its source such e.g. paid search or social
  • Data unification—Integrate with multiple online (and offline) sources such as CRM tools
  • Data analysis—Use AI to analyze phone conversations and provide insight on call drivers, behaviors and outcomes
  • Marketing integration—Push data to the marketing stack for automation, optimization, analysis and more

The end result—and key benefit—of implementing an advanced call tracking tool is to gain valuable insight about campaign performance and attribution. 

Call tracking 201: AI and machine learning 

Martech companies are increasingly powering their technology with AI-driven platforms. AI enables marketers to gain intelligence quickly and make better-informed decisions. This trend bridges multiple industries, as shown in the graphic below. 

Companies that utilize or provide AI technology for enterprises

Companies that utilize or provide AI technology—source: TOPBOTS

Invoca uses Signal AI to help measure and attribute online conversions by mining data from the phone conversations themselves, freeing up valuable time for marketers who no longer have to listen to every call.

Signal AI uses AI to detect intent and patterns in language to provide actionable insights and conversion data (sale made, appointment set, etc.) for marketers. This is accomplished through a series of steps that start with the recorded conversation, transcribing the call into text which can then be analyzed by an algorithm, identifying key patterns, phrases, and actions, and pushing these insights to your marketing stack. Here’s a visual of what that looks like. Note that Invoca does not save call transcripts and is HIPAA and PCI compliant, an important distinction for marketers concerned with data privacy.

Image source: Invoca

Signal AI uses machine learning, an application of AI, which gives machines access to the data so that they can learn from it. AI works in conjunction with machine learning to provide actionable and accessible data to marketers—but marketers still need to review this data and make decisions based on their own observations and conclusions.

Invoca offers two versions of Signal AI to their call tracking clients. Pre-trained AI uses industry-based predictive models that have been “pre-trained” using thousands of hours of call data.

Custom AI is more appropriate for certain businesses, such as those with high volumes of calls or sophisticated data needs. This more complex option takes longer to create and implement, however, it can help certain businesses predict call outcomes with a higher degree of accuracy.

Debunking some common assumptions 

Skeptics may think that humans can classify calls more efficiently and accurately than AI, but the truth is the opposite. AI learns over time and it never gets tired, so it’s an effective and accurate way to classify calls without bias. Here are some other call tracking myths, debunked:

  • It’s hard to set up AI-based call tracking—Pre-trained AI models take the guesswork out of setup for certain industries such as insurance and can identify the most common outcomes (e.g., product purchased).
  • All AI-based call tracking is the same—False! Invoca’s Signal AI uses predictive analytics (rather than just transcription) and continues to learn. It also provides performance scoring for easy reference.
  • Only big companies can afford AI-based call tracking—Wrong again. Invoca is tag-based and easy to implement. You don’t need a dedicated IT team or programmer to get up and running.

Clear strategy and clean data

The true power of AI-based call tracking is, in a word, attribution. It’s the ability to unify call data across multiple sources and attribute it to all consumer touchpoints.

Invoca does this by collecting data from multiple sources: campaign and website data, first-party data (e.g., pulled from your CRM), third-party demographic data, call data such as length, time and location of call, and conversational data (derived from speech analysis).

Once all the available data is unified, Invoca’s technology determines the value of the call by analyzing the spoken conversations within the calls. Invoca’s AI synthesizes various word patterns (e.g., “I’m almost ready to buy, but I’m waiting for XYZ to happen”) and then classifies them into useful datasets.

Signal AI helps predict the type of call (e.g., sales, service, complaint) which allows marketers to optimize media placements, ad content, and more. This level of analysis can also help inform the call experience itself by identifying issues that may frustrate callers.

Connecting call data to campaign data can help in other ways too. For example, marketers can use call information for ad suppression, making sure customers don’t see offers for something they’ve already purchased or retargeting ads to people who called but didn’t make a purchase.

Tying it all together

One of the most powerful features of the more robust, high-end call-tracking tools like Invoca is the ability for them to integrate with existing marketing platforms like Google Analytics, Adobe Experience Cloud, and Salesforce. 

This gives marketers a clear picture of where their customers are at every step of the journey. It closes the attribution loop, allowing you to demonstrate what’s working from an ROI standpoint, a metric that’s key when it comes time for approval and budget allocation.

When considering implementing a tool like Invoca, the bottom line is always the top priority—will we make money with this martech investment?

Invoca customers have seen up to 60% increase in conversions when implementing the tool (without any additional media spend), an important consideration when factoring in ROI.

The Invoca Call Tracking Guide covers all this including what questions to ask vendors when considering a new tool and what to consider when shopping for a call tracking solution.

To learn more about call tracking technology from functionality to  implementation and how call tracking can help with campaign optimization and attribution, download Invoca’s whitepaper, “The Call Tracking Study Guide for Marketers.”

The post Guide to call tracking and the power of AI for analyzing phone data appeared first on Search Engine Watch.

Search Engine Watch


Atlan raises $2.5M to stop enterprises from being so bad at managing data

July 2, 2019 No Comments

Even as much of the world is digitizing its governance, in small towns and villages of India, data about its citizens is still being largely logged on long and thick notebooks. Have they received the subsidized cooking gas cylinders? How frequent are the power cuts in the village? If these data points exist at all, they are probably stored in big paperbacks stacked in a corner of some agency’s office.

Five years ago, two young entrepreneurs — Prukalpa Sankar and Varun Banka — set out to modernize this system. They founded SocialCops, a startup that builds tools that make it easier for government officials — and anyone else — to quickly conduct surveys and maintain digital records that could be accessed from anywhere.

The Indian government was so impressed with SocialCops’ offering that it partnered with the startup on National Data Platform, a project to connect and bring more transparency within many of the state-run initiatives; and Ujjwala Yojana, a project to deliver subsidized cooking gas cylinders to poor women across the nation.

“This is a crucial step towards good governance through which we will be able to monitor everything centrally,” India’s Prime Minister Narendra Modi said of National Data Platform. “It will enable us to effectively monitor every village of the country.”

Two years ago, the duo wondered if the internal tools that they built for their own teams to manage their projects could help data teams around the world? The early results are in: Atlan, a startup they founded using learnings from SocialCops, has secured more than 200 customers from over 50 nations and has raised $ 2.5 million in pre-Series A funding led by Waterbridge Ventures, an early stage venture fund.

The startup, which employs about 80 people, has also received backing from Ratan Tata, Chairman Emeritus of conglomerate Tata Sons, Rajan Anandan, the former head of Google Southeast Asia, and 500 Startups. On Tuesday, Singapore-headquartered Atlan moved out of stealth mode.

The premise of Atlan’s products is simple. It’s built on the assumption that the way most people in enterprises deal with data is inefficient and broken, Sankar and Banka told TechCrunch in an interview. Typically, there is no central system to keep track of all these data points that often live in their own silos. This often results in people spending days to figure out what their compliance policy is, for instance.

“Atlan wants to democratize data inside organizations,” said Sankar.

Atlan Discovery 2

Teams within a typical company currently use a number of different tools to gather and manage data. Atlan has built products — dubbed Discovery, Grid, and Workflows — to create a collaboration layer, bringing together diverse data (from internal and external sources), tools and people to one interface.

“We are reimagining every human interaction with data. For instance, code has a profile on GitHub—what would a “profile” of data look like? What if you could share data as easily as a Google Sheets link, without worrying about the size or format? Or what would a data versioning and approval workflow look like? What if data scientists could acquire external data within minutes, instead of the months it takes right now?” said Banka.

The startup has also built a product called Collect that allows an organization to quickly deploy apps to collect granular data. These apps can collect data even when there is no internet connection. All of these data points, too, then find their way to the interface.

Atlan intends to use the capital it has raised on product development and sign more customers. It has already won some big names including Unilever, Milkbasket, Barbeque Nation, WPP and GroupM, Mahindra Group and InMobi in India, Chuan Lim Construction in Singapore, ServeHaiti in Haiti, Swansea University in the UK, the Ministry of Environment in Costa Rica, and Varun Beverages in Zambia.

In a prepared statement, Manish Kheterpal, Managing Partner at WaterBridge Ventures, said, “companies are struggling to overcome the friction that arises when diverse individuals need to collaborate, leading to project failure. The IPOs of companies like Slack and Zoom are proof that we live in the era of consumerization of the enterprise. With its sharp focus on data democratization, Atlan is well-positioned to reimagine the future of how data teams work.”

As for SocialCops, Sankar said it will live on as a data science community and pursue its signature “social good” mission.


Social – TechCrunch


Atlan raises $2.5M to stop enterprises from being so bad at managing data

July 2, 2019 No Comments

Even as much of the world is digitizing its governance, in small towns and villages of India, data about its citizens is still being largely logged on long and thick notebooks. Have they received the subsidized cooking gas cylinders? How frequent are the power cuts in the village? If these data points exist at all, they are probably stored in big paperbacks stacked in a corner of some agency’s office.

Five years ago, two young entrepreneurs — Prukalpa Sankar and Varun Banka — set out to modernize this system. They founded SocialCops, a startup that builds tools that make it easier for government officials — and anyone else — to quickly conduct surveys and maintain digital records that could be accessed from anywhere.

The Indian government was so impressed with SocialCops’ offering that it partnered with the startup on National Data Platform, a project to connect and bring more transparency within many of the state-run initiatives; and Ujjwala Yojana, a project to deliver subsidized cooking gas cylinders to poor women across the nation.

“This is a crucial step towards good governance through which we will be able to monitor everything centrally,” India’s Prime Minister Narendra Modi said of National Data Platform. “It will enable us to effectively monitor every village of the country.”

Two years ago, the duo wondered if the internal tools that they built for their own teams to manage their projects could help data teams around the world? The early results are in: Atlan, a startup they founded using learnings from SocialCops, has secured more than 200 customers from over 50 nations and has raised $ 2.5 million in pre-Series A funding led by Waterbridge Ventures, an early stage venture fund.

The startup, which employs about 80 people, has also received backing from Ratan Tata, Chairman Emeritus of conglomerate Tata Sons, Rajan Anandan, the former head of Google Southeast Asia, and 500 Startups. On Tuesday, Singapore-headquartered Atlan moved out of stealth mode.

The premise of Atlan’s products is simple. It’s built on the assumption that the way most people in enterprises deal with data is inefficient and broken, Sankar and Banka told TechCrunch in an interview. Typically, there is no central system to keep track of all these data points that often live in their own silos. This often results in people spending days to figure out what their compliance policy is, for instance.

“Atlan wants to democratize data inside organizations,” said Sankar.

Atlan Discovery 2

Teams within a typical company currently use a number of different tools to gather and manage data. Atlan has built products — dubbed Discovery, Grid, and Workflows — to create a collaboration layer, bringing together diverse data (from internal and external sources), tools and people to one interface.

“We are reimagining every human interaction with data. For instance, code has a profile on GitHub—what would a “profile” of data look like? What if you could share data as easily as a Google Sheets link, without worrying about the size or format? Or what would a data versioning and approval workflow look like? What if data scientists could acquire external data within minutes, instead of the months it takes right now?” said Banka.

The startup has also built a product called Collect that allows an organization to quickly deploy apps to collect granular data. These apps can collect data even when there is no internet connection. All of these data points, too, then find their way to the interface.

Atlan intends to use the capital it has raised on product development and sign more customers. It has already won some big names including Unilever, Milkbasket, Barbeque Nation, WPP and GroupM, Mahindra Group and InMobi in India, Chuan Lim Construction in Singapore, ServeHaiti in Haiti, Swansea University in the UK, the Ministry of Environment in Costa Rica, and Varun Beverages in Zambia.

In a prepared statement, Manish Kheterpal, Managing Partner at WaterBridge Ventures, said, “companies are struggling to overcome the friction that arises when diverse individuals need to collaborate, leading to project failure. The IPOs of companies like Slack and Zoom are proof that we live in the era of consumerization of the enterprise. With its sharp focus on data democratization, Atlan is well-positioned to reimagine the future of how data teams work.”

As for SocialCops, Sankar said it will live on as a data science community and pursue its signature “social good” mission.


Startups – TechCrunch


Big Data Supercharged Gerrymandering. It Could Help Stop It, Too

June 28, 2019 No Comments

The Supreme Court decided Thursday it doesn’t want to address partisan gerrymandering—but there are lots of other ways to fight it.
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Sisense acquires Periscope Data to build integrated data science and analytics solution

May 14, 2019 No Comments

Sisense announced today that it has acquired Periscope Data to create what it is calling a complete data science and analytics platform for customers. The companies did not disclose the purchase price.

The two companies’ CEOs met about 18 months ago at a conference, and running similar kinds of companies, hit it off. They began talking and, after a time, realized it might make sense to combine the two startups because each one was attacking the data problem from a different angle.

Sisense, which has raised $ 174 million, tends to serve business intelligence requirements either for internal use or externally with customers. Periscope, which has raised more than $ 34 million, looks at the data science end of the business.

Both CEOs say they could have eventually built these capabilities into their respective platforms, but after meeting they decided to bring the two companies together instead, and they made a deal.

Harry Glasser from Periscope Data and Amir Orad of Sisense.

Harry Glasser from Periscope Data and Amir Orad of Sisense

“I realized over the last 18 months [as we spoke] that we’re actually building leadership positions into two unique areas of the market that will slowly become one as industries and technologies evolve,” Sisense CEO Amir Orad told TechCrunch.

Periscope CEO Harry Glasser says that as his company built a company around advanced analytics and predictive modeling, he saw a growing opportunity around operationalizing these insights across an organization, something he could do much more quickly in combination with Sisense.

“[We have been] pulled into this broader business intelligence conversation, and it has put us in a place where as we do this merger, we are able to instantly leapfrog the three years it would have taken us to deliver that to our customers, and deliver operationalized insights on integration day on day one,” Glasser explained.

The two executives say this is part of a larger trend about companies becoming more data-driven, a phrase that seems trite by now, but as a recent Harvard Business School study found, it’s still a big challenge for companies to achieve.

Orad says that you can debate the pace of change, but that overall, companies are going to operate better when they use data to drive decisions. “I think it’s an interesting intellectual debate, but the direction is one direction. People who deploy this technology will provide better care, better service, hire better, promote employees and grow them better, have better marketing, better sales and be more cost effective,” he said.

Orad and Glasser recognize that many acquisitions don’t succeed, but they believe they are bringing together two like-minded companies that will have a combined ARR of $ 100 million and 700 employees.

“That’s the icing on the cake, knowing that the cultures are so compatible, knowing that they work so well together, but it starts from a conviction that this advanced analytics can be operationalized throughout enterprises and [with] their customers. This is going to drive transformation inside our customers that’s really great for them and turns them into data-driven companies,” Glasser said.


Enterprise – TechCrunch


A summary of Google Data Studio: Updates from April 2019

May 14, 2019 No Comments

April was a big month for Google Data Studio (GDS), with Google introducing some significant product updates to this already robust reporting tool.

For those not familiar with GDS, it is a free dashboard-style reporting tool that Google rolled out in June 2016. With Data Studio, users can connect to various data sources to visualize, and share data from a variety of web-based platforms.

GDS supports native integrations with most Google products including Analytics, Google Ads, Search Ads 360 (formerly Doubleclick Search), Google Sheets, YouTube Analytics, and Google BigQuery.

GDS supports connectors that users can purchase to import data from over one hundred third-party sources such as Bing Ads, Amazon Ads, and many others.  

Sample Google Data studio dashboard

Source: Google

1. Google introduces BigQuery BI Engine for integration with GDS

BigQuery is Google’s massive enterprise data warehouse. It enables extremely fast SQL queries by using the same technology that powers Google Search. Per Google,

“Every day, customers upload petabytes of new data into BigQuery, our exabyte-scale, serverless data warehouse, and the volume of data analyzed has grown by over 300 percent in just the last year.”

BigQuery BI Engine stores, analyzes, and finds insights on your data Image Source: Google

Source: Google

2. Enhanced data drill-down capabilities

You can now reveal additional levels of detail in a single chart using GDS’s enhanced data drill down (or drill up) capabilities.

You’ll need to enable this feature in each specific GDS chart and, once enabled, you can drill down from a higher level of detail to a lower one (for example, country to a city). You can also drill up from a lower level of detail to a higher one (for example, city to the country). You must be in “View” mode to drill up or drill down (as opposed to the “Edit” mode).

Here’s an example of drilling-up in a chart that uses Google’s sample data in GDS.

GDS chart showing clicks by month

Source: Google

To drill-up by year, right click on the chart in “View” mode and select “Drill up” as shown below.

GDS chart showing the option to “Drill up” the monthly data to yearly data

Visit the Data Studio Help website for detailed instructions on how to leverage this feature.

3. Improved formatting of tables

GDS now allows for more user-friendly and intuitive table formatting. This includes the ability to distribute columns evenly with just one click (by right-clicking the table), resizing only one column by dragging the column’s divider, and changing the justification of table contents to left, right, or center via the “Style” properties panel in “Edit” mode.

Example of editing, table properties tab in GDS

Source: Google

Detailed instructions on how to access this feature are located here.

4. The ability to hide pages in “View” mode

GDS users can now hide pages in “View” mode by right clicking on the specific page (accessed via the top submenu), clicking on the three vertical dots to the right of the page name, and selecting “Hide page in view mode”. This feature comes in handy when you’ve got pages you don’t want your client (or anyone) to see when presenting the GDS report.

The new “Hide page” feature in GDS

Source: Google

5. Page canvas size enhancements

Users can now customize each page’s size with a new feature that was rolled out on March 21st (we’re sneaking this into the April update because it’s a really neat feature).

Canvas size settings can be accessed from the page menu at the top of the GDS interface. Select Page>Current Page Settings, and then select “Style” from the settings area at the right of the screen. You can then choose your page size from a list of pre-configured sizes or set a custom size of your own.

GDS Page Settings Wizard

Source: Google

6. New Data Studio help community

As GDS adds more features and becomes more complex, it seems only fitting that Google would launch a community help forum for this tool. So, while this isn’t exactly a new feature to GDS itself, it is a new resource for GDS users that will hopefully make navigating GDS easier.

Users can access the GDS Help Community via Google’s support website or selecting “Help Options” from the top menu bar in GDS (indicated by a question mark icon) then click the “Visit Help Forum” link.

The Help menu within GDS

Source: Google

Conclusion

We hope that summarizing the latest GDS enhancements has made it a little easier to digest the many new changes that Google rolled out in April (and March). Remember, you can always get a list of updates, both new and old by visiting Google’s Support website here.

Jacqueline Dooley is the Director of Digital Strategy at CommonMind.

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