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What do online experiments, data science and product development look like at Booking.com, the world’s largest accommodations provider?

What do online experiments, data science and product development look like at Booking.com, the world’s largest accommodations provider?

Online Experiments at Booking.com

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What are the online best python ides to compile and edit? Do you know the best online python ide for Mac, Windows and Linux? Here is the list of best python ides for online compile and edit your codes. If you are a developer or beginner in developing then here is the best solution for editing your c

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Bloomberg Philanthropies has lauded a handful of local governments for their policies on collecting data about how their cities function–and making policies based on that data.

These 9 Cities Are Certified Government Data Pioneers

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Digital transformation is the use of new technology to address business problems, and includes initiatives like cloud computing, going paperless, e-commerce, blockchain, online learning, and much more.
<a href="https://www.gartner.com/smarterwithgartner/why-data-and-analytics-are-key-to-digital-transformation/">Gartner</a> says the key accelerator for digital transformation is an organization’s competency in data and analytics, and by 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.
Alarmingly, <a href="https://www.forbes.com/sites/brucerogers/2016/01/07/why-84-of-companies-fail-at-digital-transformation/#a8411e9397bd">Forbes</a> estimates that 7 out of 8 digital transformation programs fail. In this blog post, we’ll investigate what they’re doing wrong—and how you can get it right.
<h2 id="digital-transformation-starts-and-ends-with-data">Digital transformation starts and ends with data</h2>
Our VP of Corporate Sales, <a href="https://www.linkedin.com/in/shedberg">Sam Hedberg</a>, believes that a well-executed data strategy is necessary to complete a company’s digital transformation:
<blockquote>
Data is the lifeblood that pumps through your digital transformation. Without it, your digital transformation would die.
</blockquote>
<a href="https://www.datacamp.com/community/podcast/data-science-mckinsey">On Episode 30 of our podcast, DataFramed</a>, Taras Gorishnyy, then a Senior Analytics Manager at McKinsey, identified the necessary moving parts to building a data strategy:
<ol>
<li>Create a vision for analytics aligned with company strategy</li>
<li>Establish strong C-level support, e.g., advocacy, funding, and excitement</li>
<li>Build a strong data foundation to provide integrated, trusted, and timely data</li>
<li>Extract value early from several use cases</li>
<li>Implement process redesign and culture change to ensure the right mix of data skills across the company</li>
</ol>
Let’s examine how each of these steps in building a data strategy can help you successfully execute your digital transformation.
<h2 id="1-create-a-vision-for-analytics-aligned-with-company-strategy">1. Create a vision for analytics aligned with company strategy</h2>
<a href="https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/breaking-away-the-secrets-to-scaling-analytics">McKinsey</a> says that many companies start their analytics journey by finding out what data they have and determining where it can be applied. But this approach is not scalable. Instead, companies should start by identifying the decision-making processes they could improve to generate additional value in the context of their overall strategy.
These are a company’s critical strategic areas, and typically require cross-functional alignment. But sometimes, large companies begin with a data strategy of hiring a lot of data scientists and launching pilot projects. Often, these efforts ultimately don’t scale, because that’s putting the cart before the horse. Analytics teams that work in a silo will produce limited output.
One of our customers, a financial institution, realized that open source languages were the way of the future and decided that the whole company should be able to speak the language of data. So they turned to DataCamp to help them scale data skills across departments and at every level. With their company-wide upskilling initiative, they onboarded hundreds of employees, using our “teams” functionality to develop custom learning plans by department, learning objectives, and skill levels. They also create assignments for various teams, which keeps employees engaged and actively learning.
<h2 id="2-establish-strong-c-level-support">2. Establish strong C-level support</h2>
In large organizations, different departments are often not designed to work together. For a massive transformation initiative, executive sponsorship is critical to establish a unifying mission across the company.
In 2018, Salesforce conducted a <a href="https://www.salesforce.com/form/digital-transformation-strategies-for-success/?nc=7010M000000uUu5QAE?d=7010M000000uUuAQAU">study on digital transformation</a> revealing that 75% of decision makers said their digital transformation efforts were supported by executive sponsorship. Executive support in data and analytics translates to greater chance of success due to more investment dollars, a higher likelihood of execution, and greater organizational alignment.
One of our customers, a national government agency, began a digital transformation initiative this year with the support of their Commissioner. With executive support, they expect to complete their digital transformation in three years, including an overhaul of their digital platforms and a move to online learning focused on upskilling in R via DataCamp.
<h2 id="3-build-a-strong-data-foundation-to-provide-integrated-trusted-and-timely-data">3. Build a strong data foundation to provide integrated, trusted, and timely data</h2>
The common thread for all digital transformation initiatives is data. All of an organization’s systems—from process flows to applications to data centers—rely on high-quality data. Every organization needs to have the processes and capabilities to be able to use their data. This includes data quality management, such as pulling in data from various sources and cleansing it to ensure accuracy, as well as governance to ensure compliance with data privacy regulations like GDPR and CCPA.
This is why our co-founder and CEO Martijn Theuwissen said in an interview with <a href="https://www.aithority.com/interviews/ait-megamind/aithority-interview-with-martijn-theuwissen-co-founder-at-datacamp/">AiThority</a>: “Companies need solid data foundations and tools for extracting, loading, and transforming data (ETL), as well as tools for cleaning and aggregating data from disparate sources.”
And it’s why Dr. Hugo Bowne-Anderson, data scientist and educator at DataCamp, believes <a href="https://www.oreilly.com/radar/the-unreasonable-importance-of-data-preparation/">data preparation is just as important in the data workflow</a> as machine learning, deep learning, and AI. After all, “your models are only as good as your data.” The nuts and bolts can’t be overlooked—they must be carefully sourced, installed, and tested for your data engine to run properly.
<h2 id="4-extract-value-early-from-several-use-cases">4. Extract value early from several use cases</h2>
In order for enterprises to execute an ambitious data strategy that delivers real business results, they need to be able to point to successful use cases. Demonstrating successful use cases is critical to communicate value and urgency across the entire organization. <a href="https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/digital-blog/how-to-maximize-the-returns-from-your-data">McKinsey</a> says that projects must demonstrate value early and often through quick wins, so different data initiatives may be happening in parallel:
<blockquote>
[A] data executive explained how, rather than immediately try to connect disparate systems or create one master system, which could take years, he placed a “layer” over the existing systems that presented a complete view of a process, such as the lending process at a bank, that takes place via multiple systems. He created alerts to signal when steps in the process occur (such as loan approval, when an email is sent to the applicant notifying him or her of the approval, and so on), so that even if each step was completed by a different system, teams could see the whole process as if the systems are working together.
</blockquote>
This example demonstrates that extracting value from and collaborating with data is closely tied to the previous step of building a strong data ecosystem.
<h2 id="5-implement-process-redesign-and-culture-change">5. Implement process redesign and culture change</h2>
ShopRunner CEO, Sam Yagan, believes his company <a href="https://assets.mckinsey.com/business-functions/mckinsey-analytics/our-insights/building-an-innovative-data-driven-culture-an-interview-with-shoprunner-ceo-sam-yagan">has incorporated data fluency</a> in their culture:
<blockquote>
Because data is so infused in our culture, it doesn’t take long before someone realizes that the person sitting next to them or the person sitting across from them is running his or her own reports and accessing his or her own data.
</blockquote>
DataCamp can help every company achieve this gold standard of company-wide data fluency. Our online learning environment supports data strategy execution and helps companies democratize data fluency—at any level, and across every department.
We conducted <a href="https://datacamp.foleon.com/enterprise/ld-whitepaper/cover/">a survey</a> of over 300 Learning and Development (L&amp;D) leaders, and found that building data fluency is a priority for 89% of companies. The key to achieving this at scale is not just by hiring top talent—it’s by creating a culture of upskilling and reskilling your workforce.
By 2017, <a href="https://elearningindustry.com/top-elearning-statistics-2019">77% of US companies</a> were already using online learning. With so much of the world population working from home today, online learning has never been more relevant.
Online learning is a key part of digital transformation, and DataCamp provides a learn-by-doing approach to democratize data skills across your company. Visit <a href="https://datacamp.com/discover/enterprise">datacamp.com/discover/enterprise</a> to find out more about how we can help you execute a scalable data strategy to complete your company’s digital transformation.
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https://www.datacamp.com/community/blog/digital-transformation

Digital transformation starts and ends with data. 

Why Your Digital Transformation Will Fail if You Don’t Have a Data Strategy

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Machine learning, deep learning, and AI are all buzzwords for good reason: these technologies are fundamentally shifting the nature of business, society, and our lives. More importantly, across many verticals, they’re shifting from being disruptive technologies to being foundational and table stakes for businesses to remain competitive.
<h2 id="the-power-of-machine-learning-across-verticals">THE POWER OF MACHINE LEARNING ACROSS VERTICALS</h2>
To get started, let’s look at several key examples of how machine learning (ML) is impacting various verticals:
<ul>
<li>In tech, ML powers recommendation systems, content discovery, search engines, email spam filters, and matching problems.</li>
<li>In healthcare, machine learning facilitates drug discovery and diagnostic imaging diagnosis.</li>
<li>In finance, ML is now foundational for fraud detection, process automation, algorithmic trading, and robo-advisory.</li>
<li>In retail, Walmart is at the forefront of using ML to reinvent supply chain management.</li>
<li>Burgeoning industries such as LegalTech and AgTech (Agriculture Technology) are growing rapidly and employing machine learning. Legal technologists are imagining a future in which ML is leveraged to predict outcomes of court cases based on natural language analysis of precedents. And in agriculture, drones are being deployed at scale to capture footage, and ML is being used to estimate crop yields.</li>
</ul>
<h2 id="the-power-of-machine-learning-across-teams">THE POWER OF MACHINE LEARNING ACROSS TEAMS</h2>
The above examples are vertical-specific, but there are also many gains in efficiency in the development of ML algorithms that are vertical-independent, such as:
<ul>
<li>HR teams filtering applicants in the hiring flow.</li>
<li>Support teams using ML for call center routing.</li>
<li>Marketing teams using ML algorithms for paid advertising, customer churn prediction, and targeted nurture campaigns.</li>
</ul>
In fact, any company that has an app can benefit from leveraging ML to determine the most effective push notifications, and any organization that has a website can leverage ML to personalize their customer experience by surfacing content and features that are most relevant to them.
According to <a href="https://www.gartner.com/smarterwithgartner/a-data-and-analytics-leaders-guide-to-data-literacy/">Gartner’s Annual Chief Data Officer Survey</a>, poor data literacy is the second-biggest internal roadblock to the success of chief data officers. Gartner expects that, by 2020, 50% of organizations will lack sufficient AI and data literacy skills to achieve business value, and 80% of organizations will initiate targeted data literacy initiatives to overcome deficiencies. The data is clear: To keep your competitive advantage, you’ll need to leverage ML in one form or another. The question is, as a business leader, what do you need to know about it?
<h2 id="how-to-slice-data-analytics">HOW TO SLICE DATA ANALYTICS</h2>
The first thing to do is to place ML in the broader context of what data analytics and data science can offer. One instructive way of slicing the data science space is into the following areas:
<ul>
<li>Descriptive analytics is essentially about getting the right pre-existing data in front of the right people in the form of dashboards, reports, or emails. This can include both past and real-time time data about revenue, customer engagement, churn, and company and employee performance.
</li>
<li>Predictive analytics is synonymous with ML and is the realm of predicting the future (such as whether a customer will churn or not) and more general classification tasks (such as whether an email is spam or not, and if a tumor is benign or malignant).
</li>
<li>Prescriptive analytics is the realm of decision science and how to make decisions based on data. If your ML model tells you that a particular customer will churn, you’ll want to know what to do about it. Prescriptive analytics is concerned with finding frictionless interfaces between the data and decision functions in any organization. Exciting spaces to watch are <a href="https://www.mckinsey.com/business-functions/mckinsey-analytics/careers/translator">data translation</a> (a burgeoning field for those with both domain expertise and technical know-how), advances in reinforcement learning (which bleeds into ML; see below), and the work of Cassie Kozyrkov, Chief Decision Scientist at Google Cloud, with whom I discussed <a href="https://www.datacamp.com/community/blog/decision-intelligence-data-science">decision science on DataFramed, the DataCamp podcast</a>.
</li>
</ul>
<h2 id="types-of-machine-learning">TYPES OF MACHINE LEARNING</h2>
Now that we have a sense of the data space, let’s dive into the different types of machine learning:
<ul>
<li>Unsupervised learning is about discovering general patterns in data, the most popular example being clustering or segmenting customers and users. This type of segmentation is generalizable and can be applied broadly, e.g. to documents, companies, and genes.</li>
<li>Supervised learning currently enjoys the lion’s share of ML, and is concerned with the prediction and classification of data.You may notice that when people talk about ML, they’re probably specifically focused on supervised learning.</li>
<li>Reinforcement learning is concerned with training ML models to make decisions. Examples include many promising algorithms for self-driving cars and AlphaGo Zero, which was not trained on any human game data and was able to achieve superhuman performance in Chess, Go, and Shogi after 24 hours of training.</li>
</ul>
In the short to medium term, supervised learning will be the most important ML technique for business leaders so we’ll focus on it in subsequent posts. However, do keep reinforcement learning on your radar.
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Find out how buzzwords like machine learning, deep learning, and AI are fundamentally shifting the nature of business, and how you need to think of them to stay competitive.

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