When you build products and launch them, are you – and be honest here – making decisions on each stage of development using data?

While for many years, everyone from development teams, product managers, founders, and marketers have touted they’re using a data-driven approach to everything; the truth is often starkly different.

Partly, that’s because we’re just creating what we technically can. Partly it’s because the wrong strategies, tactics, and foundations have been put in place, and processes aren’t followed.

With this in mind, The Tesseract Academy is delivering a free short introductory workshop on April 19, 2021, designed to help you make a real impact with your current or next product.

This is a free short introduction to the data-driven product boot camp delivered by Noam Auerbach. The full boot camp’s goal is to help product managers and other product practitioners level their decision-making skills. The free intro will present some case studies, such as how Soundcloud is building data-driven products. A Q&A session will follow where the participants can ask any question they like or get help with any issue related to data-driven product development.

Noam Auerbach is a Data Product Manager and consultant with 10+ years of experience in the field.

Noam is focused on scaling and monetizing platforms. He has been on the fine line between product management and growth throughout his career, driving retention and revenue. At the moment, he is the head of product & growth at Enhancv – the world’s leading resume builder and career development platform. Before that, he was the product lead at Tourlane (a Soquia Berlin-based traveling start-up), head of product at YEAY (Berlin-based video social shopping app), and the Growth PM at SoundCloud.

This event is perfect for CEOs, founders, managers, entrepreneurs, and product managers, which will equip you with tools and techniques to build agreement as you create your product strategy and roadmap.

Anyone interested in the free introductory workshop by The Tesseract Academy can simply register on Eventbrite.

Where do I get started with data-driven engineering? How can the 3 I’s of data-driven engineering help me get off to a running start? How can I avoid the common pitfalls of data-driven engineering?

What are the 3 I’s?

The 3 I’s of data-driven engineering are insights, indicators and investments.

What are insights?

Insights are observations we derive from data generated by the software under test. They are the equivalent of observations we make about the product in the world of software testing. In software testing, we observe the software behavior, and we summarize these observations as feedback to the team. Insights are no different. We are still observing the software behavior. However, we are focusing on different ways of observing software behavior (e.g. logs, telemetry, CPU stats). In my mind, getting insights about our software is software testing.

What are indicators?

Indicators tell us that something could potentially be wrong. They indicate to us that an investigation and analysis should be done.

What are investments?

Investments are what we do based on our insights and indicators.

Why are the 3 I’s important? Why should I do this?

With the 3 I’s under your belt, you now have a framework to attack an engineering or testing problem driven by data, but why do this at all? Why not just put up a dashboard of indicators, call it a day and let the engineering team see the issues so that they fix them?

In an ideal world, all issues would be addressed as soon as they are surfaced. Hidden inside insights are 2 more I’s: investigations and interpretation. Even with the best indicators, data-driven engineers do investigations to interpret the indicators. They may dig into related data, try to correlate this with other indicators or trace through the source code. Based on the insights collected from investigating and interpreting the results, data-driven engineers push for investments or make the investments themselves.

Couldn’t all of this be automated?

Of the 3 I’s, indicators are well-suited for automation. Putting up a dashboard of indicators on a real-time basis is a job best done by a computer. However, the role of designing the indicator, deriving insights from investigations, and interpreting those insights into actionable investments is best suited for… you guessed it, a data-driven engineer.

Of all the 3 I’s (5 if you include investigations and interpretation), displaying indicators for the team can be easily automated, but other activities are uniquely human.

How can I get started?

Apply the 3 I’s!

Software testers are continuously questioning the product… Does it break when stress it this way? Does it allow me access to something I have no permission for? What if change the order of how I do certain steps?

Data-driven engineers are continuously questioning the product, too! What are the top server errors? What are the top client crashes? How many users do these errors impact? How many users have stopped using the product? How many new users are using it? Why are we seeing these trends?

Start with something you want to know about the product, and try to answer it with the data available. If it’s not available, then your investigation just yielded an insight! Take that insight and translate it into an actionable investment e.g. instrument the code so that we have the data to answer our question.

What are common mistakes?

You put up an indicator, because you have it available. Insights and indicators are tied together. The indicator must mean something to the business. For example, CPU usage is a nice well-defined indicator, but what does it mean to the user? Do we have data that says that increased CPU usage results in customers eventually giving up on the software? What’s the threshold for when this really becomes a problem?

You leave out the insights and investments. Your indicators, data and findings are required for data-driven engineering. However, your insights and investments are even more important. What opinion did you form based on all the indicators and data? What insights did you derive from your investigations? What should we invest in based on the interpretations? Link your findings to the business, and all will be right in the world.

This post originally appeared on rayli.net

Like this article? Subscribe to our weekly newsletter to never miss out!

Follow @DataconomyMedia

Every business knows that their data, from customer demographics and buying behavior to production insights, holds tremendous value. But, historically, most have considered only its internal worth—the insight it provides to improve operational efficiency, deliver a better customer experience, enhance products and services or save money.

But now, a new perspective on data is emerging. Not only is the data valuable for internal purposes, but it can even be sold by companies who are establishing a data-as-a-service model, and the examples are quite surprising. Businesses of all types are exploring the market potential of their data, considering ways to productize, package and profit from it—how they can literally turn their data into dollars – within the security, privacy and regulatory guidelines.

What can your data do for you?

The ability to collect and analyze rich, dynamic data—made possible through cloud computing, massive data warehousing systems and business intelligence solutions—could turn companies many would never consider as being in “the data business” into potentially lucrative purveyors of information.

A toothbrush manufacturer, for example, knows a tremendous amount about toothbrush consumption, including how often tooth brushes are replaced and which SKUs sell best in specific geographic regions or to certain demographics. This unlikely “data business” could package and sell this insight to retailers, who would pay to obtain this insight, to help them optimize SKUs, determine which products will most likely succeed in one location versus another, or to help them turn over inventory faster.

As another example, medical laboratories conduct millions of screenings daily, providing accurate, timely results back to health care providers and consumers. These labs could anonymously analyze that data to uncover patterns and insights that would be extremely valuable to both health care organizations seeking ways to reduce and prevent disease and to pharmaceutical companies developing and marketing new medications. The data might also reveal that specific patient groups obtain screening exams at certain times during the year, data that physicians and hospitals could use to market their services to the right patients at the right time.

A “perfect storm” for data-driven business opportunities

We’re seeing the trend toward uncovering the profit potential within data stores across many business sectors. Companies like Uber, Airbnb and FitBit all provide an interesting product or service, but their true value lies in the data they collect and leverage: insight about our commute schedules, travel habits, physical activity levels and overall health. And the potential value of this data is being amplified by a perfect storm of factors:

• Mobile devices—We can now collect, track and analyze data wherever and whenever consumers go.
• Internet of Things—both wearables and machine sensors have become ubiquitous, low-cost data-capturing juggernauts.
• Global perspective — Our communications—and the data collected over the platforms we use(Facebook, WhatsApp, Twitter, etc.)—are truly global in nature.
• Cloud/SaaS platforms—no longer are our data efforts confined to the premises, behind-the-firewall. Instead, our capacity to collect, analyze, share and visualize data from many diverse sources seems infinite.

Dealing with the data deluge

Not only do we have access to more data than ever before, it also has the potential to be exponentially richer in context and meaning. It is attributable directly back to its source, where we can compare and contrast it with other attributes and characteristics. But, in order to uncover the richness of data and derive its monetary value, companies must:

1. Gain access to complete data, integrated from a wide variety of sources, formats, languages and protocols.
2. Effectively manage the data to ensure its quality. This includes verifying, cleansing, harmonizing, and storing it properly for analysis.
3. Analyze the data, for which there is no shortage of algorithms and options, almost all of which require in-depth analytical skills and talent.
4. Use business intelligence tools to visualize and gain actionable insight. This relies on a significant assumption that the data is complete, managed properly and analyzed accurately. Otherwise, it’s garbage in, garbage out.
5. Assure data security and regulatory compliance across all of these steps.

There are a multitude of technologies and approaches available to help enterprises achieve these objectives. But even with these tools, companies still struggle to derive the full value from their data for their own operational insight, much less for taking advantage of new revenue-generating opportunities. In fact, most companies are analyzing only 12 percent of their data. Even more disheartening, the ROI on Big Data projects is abysmal, with companies expecting an average of $3.50 return per $1 invested, but realizing just $0.55. Not only is the ROI lackluster, one-third of business leaders say they’re so uncertain about the accuracy of their analysis, they don’t trust it to make decisions.

The failure lies in the overwhelming complexity of dealing with multiple tools and processes. Most traditional approaches segregate data integration and data management as two separate endeavors,despite their interdependency. And, most integration efforts focus on applications—making disparate software work together—leaving data integration as somewhat of an afterthought, often resulting in even more siloed, dirty or duplicated data. The situation is compounded by the fact that most solutions are self-service. This leaves companies to do the highly technical and complex integrations themselves -while under immense pressure to move with speed and agility to stay ahead of the competition—and to stay within a cost-constrained budget.

Given these obstacles, it’s no surprise that it’s nearly impossible for companies to gain the visibility and deep insights they need to serve their internal needs, let alone uncover opportunities to monetize their data.

dPaaS is the answer

As companies demand more cohesive, integrated solutions to generate data-driven revenue, a new approach to data management is bringing all of these pieces together in a much simpler, tightly integrated way. dPaaS, or Data Platform-as-a-Service, puts data at the center of the process, resolving the inherent problems of data quality, harmonization and integration, in a fully managed environment. The dPaaS difference lies in it’s unique approach that combines:

1. A multi-tenant cloud platform, which provides greater agility, resources and scalability to handle the growing complexity and quantity of data.
2. A single, unified platform that eliminates the piecemeal approach and simplifies integration and data management.
3. Full visibility into the data—with complete tracking to see exactly where it’s come from, how it was modified, what was analyzed, how it looks today and where it’s going—instead of a black-box solution that leaves analysts in the dark.
4. Simple API management that allows businesses to do whatever they want with their data—construct packages, apply algorithms and insights, visualize, etc.—with complete flexibility.

In short, with dPaaS, companies can get down to the business of extracting value from their data, rather than spending so much time wrestling with it. With faster, more efficient time to insights, companies that never considered themselves a data purveyor, can open an entirely new revenue stream to maximize the value of—and monetize—their data.

Like this article? Subscribe to our weekly newsletter to never miss out!

Follow @DataconomyMedia

The Internet of Things (IoT) has taken off and is slowly revolutionising the world we live in. Smartphones, smart cars, even smart fridges – all now boast connectivity designed to make our lives easier and more efficient. This is expanding to a citywide scale, with over one billion connected devices currently in use in smart cities around the world (a number expected to grow to ten billion by 2020). One of the key drivers of this revolution is the increasing use of identity management amongst these smart devices, allowing them to communicate directly with one another (as well as with people) for the first time. So, with this in mind, what areas of our cities are likely to benefit most from the IoT revolution?

Below are five core aspects of city living where the growth of identity-driven IoT and smart devices is likely to have the most significant impact on citizens’ lives in the near future:

1. Transport

The transport and travel sector will reap the benefits of the smart city initiative immensely. Gone will be the days of sitting in sluggish traffic, or tirelessly driving round and round the multi-story car park searching for a space. Connected smart traffic systems will be able to monitor and collect real-time traffic information, traffic volume/flow, speeds, and hazards. This data will then be sent directly to commuters (via their unique digital identities) to warn of delays on their usual routes and suggest better alternatives. This same data will also be used to pinpoint regular traffic trends and black spots, helping city planners to develop efficient plans for improved transport infrastructure in the future. This initiative has already started to roll out in the UK; for example, Manchester has introduced ‘talkative bus stops’ as part of its £10m Smart City plan.

Give a smart city an inch and it’ll take a mile. Smart parking measures can also be brought into play. Smart meters will monitor parking availability, notifying drivers of free space locations as soon as they enter the vicinity. Once parked, smart payment systems can time the duration of the stay, capping it as soon as the car is moving again. Charges incurred can then be automatically paid via a pre-registered account, removing the need to queue at payment machines or carry large amounts of change for parking meters.

2. Sanitation

Another benefit of utilising connected devices in the IoT is the improvement it can bring to overall city cleanliness and sanitation. Thinking with the outlook that no ‘thing’ is too big or too small to have its own digital identity, public bins fitted with smart sensors could be used to alert council refuse collectors when they are full and need emptying; an initiative that has already been introduced in Milton Keynes and Camden. A network of ‘smart bins’ would help to improve the efficiency of rubbish collection routes throughout the city, preventing build up and significantly improving hygiene as a result.

On a more personal level, strategically placed motion sensors linked to a smart meter could be used to alert homeowners to any pest/rodent infestations in their homes. If required, pest control specialists could be automatically contacted to deal with issues as soon as they arise, saving nasty shocks (and potentially costly repairs) further down the line, and preventing the infestation from spreading.

3. Energy Saving

Many of us have already installed a smart home hub that can automatically regulate temperature and/or be accessed remotely by homeowners. But why stop there? The same smart hub system can be deployed on a citywide scale, used to monitor much larger public spaces such as museums, office buildings, and shopping centres. Glasgow has introduced energy efficiency through the smart city initiative, by connecting the city’s energy grid to buildings with smart capabilities, in order to manage energy consumption. In addition to temperature monitoring, smart sensors can also be used to make significant energy savings in areas such as lighting or escalator use in public spaces by ensuring the systems are only activated when citizens are in the vicinity.

4. Emergency Response

We are already coming to see how identity-driven IoT and Smart Cities can help make our lives easier on a day-to-day basis, but could they be utilised to save lives? For instance, if a smart fire detector in a building picks up smoke, it can immediately send an alert to the nearest fire station, instigating a series of pre-planned emergency response measures. As the emergency services make their way to the incident, the collaboration with smart transport meters allows them to receive a real-time update on traffic, avoiding any congestion. Similarly, their unique vehicle identities can be tracked and traffic lights automatically changed as they approach to ease their journey to the incident site. Each stage of what could have been a complicated operation is made simple and efficient. This kind of initiative is already being introduced in the UK – for example, through ‘Uber for fire engines’, recently launched in London.

5. Overall Public Safety

Many of the above-mentioned technologies will help to make cities a safer place to live by protecting citizens from issues including acts of God, transport overcrowding, and poor sanitation. However, this is just the tip of the iceberg of what identity-powered IoT can do. The same smart monitors used to save energy in the home could also detect gas leaks, alerting key parties to the leak based on key variables such as time of day, location of the homeowner, and severity of the leak. If the leak is not severe and the homeowner is within a five-minute radius, emergency services would not need to be alerted as well. This level of automated situational analysis means emergency services would avoid unnecessary call outs and can remain available should another more serious situation develop elsewhere.

Similarly, other smart systems can be deployed in different ways to help improve overall public safety within a city. For instance, smart street lighting can be used to deter street crime by increasing lighting intensity and alerting authorities if significant/unusual movement is detected in the vicinity or suspicious noises are heard.

In making our cities smarter, not only do we make our lives easier and more efficient, but we also make them safer. As the IoT continues to adopt an increasingly identity-driven approach, a wealth of new opportunities is opening up that can do all of the above and so much more. For the citizens who live, work, and socialise in smart cities, there are exciting times ahead.

Like this article? Subscribe to our weekly Newsletter.