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T-Mobile, Sprint ready board committees to decide on merger: sources

(Reuters) – T-Mobile US Inc and Sprint Corp are laying the groundwork for special committees of their board of directors to decide on a merger between the third and fourth largest U.S. wireless carriers, according to people familiar with the matter.

FILE PHOTO: Smartphones with the logos of T-Mobile and Sprint are seen in this illustration taken September 19, 2017. REUTERS/Dado Ruvic/Illustration/File Photo

These board committees are important for the merger because T-Mobile and Sprint are majority owned by Germany’s Deutsche Telekom AG and Japan’s SoftBank Group Corp respectively, and could be left vulnerable to potential lawsuits from minority shareholders if they don’t establish independent mechanisms to review the deal.

Both T-Mobile and Sprint have formed committees comprising independent board directors to decide on whether the deal should be signed once the merger agreement has been finalized, which is currently expected in the next three weeks, the sources said.

The companies’ special board committees have also hired financial advisers to help them deliver fairness opinions, the sources added.

As with many all-stock mergers, T-Mobile and Sprint have decided there is no need to give their minority shareholders a vote on the deal, the sources said.

An alternative would have been to make the merger subject to approval by a majority of their minority shareholders. However, the companies’ advisers have determined this is not legally necessary, and could even jeopardize the deal were minority shareholders to organize against it, according to the sources.

Some T-Mobile minority shareholders believe Sprint should not be offered any premium for its shares, the sources said. However, T-Mobile and Sprint have tentatively agreed on a range for a stock exchange ratio which, even at its low end, would offer Sprint a modest premium to where its shares are trading currently, the sources added.

This could result in SoftBank and other Sprint shareholders holding close to 40 percent of the combined company based on where the shares are currently trading, the sources added. The exact share exchange ratio will be determined by looking at the volume-weighted average stock price of the companies over the last few months, one of the sources added.

T-Mobile’s and Sprint’s due diligence on each other is almost complete, and much of their focus now is on working out a business plan for the combined company, as well as an integration strategy, according to the sources.

Sprint and T-Mobile declined to comment, while Deutsche Telekom and SoftBank did not immediately respond to requests for comment. The sources asked not to be identified because the negotiations are confidential.

Reporting by Liana B. Baker in New York; Editing by Muralikumar Anantharaman

Our Standards:The Thomson Reuters Trust Principles.

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Get the most out of your marketing stack by unifying data sources

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When it comes to getting the most value out of data, successful companies take a practical approach, first defining their own data strategy and then determining the tools needed to get it done. A good example of this is Airbnb, which set their own data strategy and tools to help users more accurately price their home listings. Too often, however, companies fail to lay out a clear strategy, instead relying on the available tools to show them where they need to go. Unfortunately, these tools usually serve up packaged metrics with data that is too detailed and lacks cohesion.

The mobile marketing data landscape

marketing data landscape mobile

In VentureBeat’s The State of Marketing Analytics: Insights in the age of the customer, author Jon Cifuentes writes:

“Enterprises are stuck between fragmented data silos…There’s customer data, inventory data, log data, search data, reporting, analytics, CRM, session data, et. al – with different vendors supporting each. While “real-time” customer data sounds nice in theory, the actual process of broadcasting this information through the organization is time-consuming, expensive, fragmented, and frustrating.”

These cobbled-together sources and tools provide directional insight but don’t align with initial expectations, particularly as companies start requiring custom insights and metrics.  In fact, most companies quickly find themselves in exactly the situation they had hoped to avoid – working in increasingly complex systems with considerably higher non-value added workloads.

data silo

The challenge for companies is: how do you align your data vision with your unique acquisition, engagement and monetization strategies?

Purpose-built tools like app analytics, A/B testing, marketing automation, etc. have done a great job in recent years of allowing non-technical people to analyze data, run tests and engage users. However, since these tools were built for single-use cases and by separate companies with proprietary data stores, they have failed to address a core issue: the need to access the same user data in order to truly provide a personalized experience to each user.

Data-capture tools and user engagement tools also need to be integrated in order to provide a full picture of how changes impact the product downstream.  For instance, teams need to be able to apply user actions from app analytics to run A/B tests, which will in turn impact the user experience.

The path forward

The solution exists at the platform level: unifying data sources before applications are built on top of them, with a flexible 2-way structure that enables real-time integration between event and user data, at all levels in the stack, and not just based on basic pre-determined rules with segmentations on top.

data silo2

This type of structure makes it possible for events to be enriched by boundless user attributes (user state) and enables contextual analytics.  This, in turn, produces a robust targeting framework, because now the user state can be updated in any manner, in real-time.  For example, Glassdoor utilizes this methodology to deliver real-time dynamic notifications of job alerts to users based on their prior behavior when browsing the Glassdoor website.

While many marketing vendors are fighting to define themselves as integrated or unified marketing platforms, most still need to reach deeper down the stack and unify product and marketing tools with data tools at a platform level.   Because they refer to the same data source, there will be no discrepancies between insights and actions.  For example, segments defined for analytics will maintain the same properties in A/B testing or content delivery.  Applications developed on top of unified data platforms will be inherently more flexible and manageable.

From VentureBeat

VB just released The State of Marketing Analytics: Insights in the age of the customer. $ 499 on VB Insight, or free with your martech subscription.

Omniata is coming out of beta on September 24th!  You can reach us at [email protected] to learn more. Though just coming out of beta, we’re already tracking 300 million monthly active users, 2 billion events per day, and handling over 17,000 requests per second!

Alex Arias is the CEO and cofounder of Omniata, a unified data, analytics and user engagement platform.  For more than 10 years, Alex has been an entrepreneur and driver of innovation in digital services, working previously at Digital Chocolate and EA.  He’s been helping companies define their own Data Value Journey since cofounding Omniata two and a half years ago.



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Cloudera is building a new open-source storage engine called Kudu, sources say

Cloudera at the Black Hat conference in August.

Big data company Cloudera is preparing to launch major new open-source software for storing and serving lots of different kinds of unstructured data, with an eye toward challenging heavyweights in the database business, VentureBeat has learned.

The storage engine, Kudu, is meant as an alternative to the widely used Hadoop Distributed File System and the Hadoop-oriented HBase NoSQL database, borrowing characteristics from both, according to a copy of a slide deck on Kudu’s design goals that VentureBeat has obtained. The technology will be released as Apache-licensed open-source software, the slides show.

Cloudera has had one of its early employees leading a small team to work on Kudu for the past two years, and the company has begun pitching the software to customers before an open-source release at the end of this month, a source familiar with the matter told VentureBeat.

From VentureBeat

Get faster turnaround on creative, more testing, smarter improvements and better results. Learn how to apply agile marketing at our roadshow in SF.

That source and others believe Kudu could present a new threat to data warehouses from Teradata and IBM’s PureData (formerly Netezza), and other vendors. It may also be used as a highly scalable in-memory database that can handle massively parallel processing (MPP) workloads, not unlike HP’s Vertica and VoltDB, the sources say. And one day Kudu — which works across multiple data centers with RAM and fast solid-state drives (SSDs) — could even play a part in backup and disaster recovery.

Cloudera declined to comment.

However Cloudera chooses to market Kudu, it’s clear that the software is a big step forward for the company, not only in the company’s efforts to outdo other Hadoop vendors, but also in its quest to become a longstanding player in enterprise software.

Not that Cloudera is a nobody. It’s worth almost $ 5 billion, according to one recent estimate, it has considerable backing from Intel, and it’s been positioning itself as a competitor to much larger database companies, like IBM and Oracle. But the fact is, fellow Hadoop vendor Hortonworks has gained credibility after it went public last year, and Hadoop company MapR is still around, too.

Cloudera recently doubled down on the rising Apache Spark open-source big data processing framework, but Spark is something Cloudera has been working on for years. And a few months ago, Cloudera brought new Python capability to Hadoop, following its acquisition of DataPad last year. Those are important efforts, but Kudu is something entirely new, something that can give the company freshness as it grows toward an initial public offering.

So what is Kudu, then?

It’s “nearly as fast as raw HDFS for scans” and, at the same time, “nearly as fast as HBase for random access,” according to one slide from a presentation on Kudu’s design goals. But Kudu is not meant to be a drop-in substitute for HDFS or HBase. “There are still places where these systems will be optimal, and Cloudera will continue to support and invest in them,” a slide says.

Kudu could be used for time-series data, or real-time reporting, or model building, according to another slide.

And it’s important to note that Kudu isn’t a SQL query engine for pulling up specific data. Cloudera has Impala for that, and others have Hive for that. Kudu has an “early integration” with Impala, and Spark support is coming, according to a slide.

The Kudu application programming interface (API) works with Java — the common language of Hadoop — as well as C++. Kudu’s architecture allows for operation across sites, according to one slide. That makes it comparable to Google’s Spanner and the Spanner-inspired CockroachDB. That could make Kudu a great choice for big companies looking to store their big data around the world.

Is Kudu well adopted, though? No, not yet.

“Looking for beta customers,” a slide says.

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