Sílvia Raposo

Xpand IT enters the FT1000 ranking: Europe’s Fastest Growing Companies

Xpand IT proudly announces our entry into the Europe’s Fastest Growing Companies ranking, compiled by renowned international journal the Financial Times! With sustained growth surpassing 45% in 2018, Xpand IT attained a place among the fastest growing companies, along with 1000 other European enterprises, taking into account their consolidated results between 2014 and 2017.

An income of 10 million and 195 collaborators were the figures that guaranteed our place on this list. Our income has since taken the leap to 15 million, and we can now count on the tireless work of more than 245 collaborators. And so, out of the three Portuguese tech companies distinguished with a spot on the ranking, Xpand IT can boast the best results in terms of income and the acquisition of new talent.

Paulo Lopes, CEO & Senior Partner of Xpand IT, said “Having a place on the FT 1000 European ranking is the ultimate recognition for all the work we have undertaken over the last few years.  We are renowned for our know-how and expertise within the technology arena, and now also for our unique team and business culture, focused on excellency and innovation, which makes it far easier to achieve these kinds of results.”

This year’s goal is to maintain our growth trend, not just by expanding into new markets, but also by increasing our workforce. In 2019, we expect to reach the beautifully rounded number of 300 Xpanders!

Sílvia RaposoXpand IT enters the FT1000 ranking: Europe’s Fastest Growing Companies
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7 steps to implement a data science project

Data science is a set of methods and procedures applied to a very complex, concrete problem, in order to solve it. It can use data interference, algorithm development and technology to analyse collected data and understand certain phenomena, identifying patterns. Data scientists must be in possession of mathematical and technological knowledge, along with the right mindset to achieve the expected results.

Through the unification of various concepts, such as statistics, data analysis and machine learning, the main objective is to unravel behaviours, tendencies or interferences in specific data that would be impossible to identify via a simple analysis. The discovery of valuable insights will allow companies to make better business decisions and leverage important investments.

In this blog post, we unveil 7 important steps to facilitate the implementation of data science.

1. Defining the topic of interest / business pain-points

In order to initiate a data science project, it is vital for the company to understand what they are trying to discover. What is the problem presented to the company or what kind of objectives does the company seek to achieve? How much time can the company allocate to working on  this project? How should success be measured?

For example, Netflix uses advanced data analysis techniques to discover viewing patterns from their clients, in order to make more adequate decisions regarding what shows to offer next; meanwhile, Google uses data science algorithms to optimise the placement and demonstration of banners on display, whether for advertisement or re-targeting.

2. Obtaining the necessary data

After defining the topic of interest, the focus shifts to the collection of fundamental data to elaborate the project, sourced from available databases. There are innumerable data sources, and while the most common are relational databases, there are also various semi-structured sources of data. Another way to collect the necessary data revolves around establishing adequate connections to web APIs or collecting data directly from relevant websites with the potential for future analysis (web scrapping).

3. “Polishing” the collected data

This is the next step – and the one that comes across as more natural – because after extracting the data from their original sources, we need to filter it. This process is absolutely essential, as the analysis of data without any reference can lead to distorted results.

In some cases, the modification of data and columns will be necessary in order to confirm that no variables are missing. Therefore, one of the most important steps to consider is the combination of information originating from various sources, establishing an adequate foundation to work on, and creating an efficient workflow.

It is also extremely convenient for data scientists to possess experience and know-how in certain tools, such as Python or R, which allow them to “polish” data much more efficiently.

4. Exploring the data

When the extracted data is ready and “polished”, we can proceed with its analysis. Each data source has different characteristics, implying equally different treatments. At this point, it is crucial to create descriptive statistics and test several hypotheses – significant variables.

After testing some variables, the next step will be to transfer the obtained data into data visualisation software, in order to unveil any pattern or tendency. It is at this stage that we can include the implementation of artificial intelligence and machine learning.

5. Creating advanced analytical models

This is where the collected data is modelled, treated and analysed. It is the ideal moment to create models in order to, for example, predict future results. Basically, it is during this stage that data scientists use regression formulas and algorithms to generate predictive models and foresee values and future patterns, in order to generalise occurrences and improve the efficiency of decisions.

6. Interpreting data / gathering insights

We are nearly entering the last level for implementing a data science project. In this phase, it is necessary to interpret the defined models and discover important business insights – finding generalisations to apply to future data – and respond to or address all the questions asked at the beginning of the project.

Specifically, the purpose of a project like this is to find patterns that can help companies in their decision-making processes: whether to avoid a certain detrimental outcome or repeat actions that have reproduced manifestly positive results in the past.

7. Communicating the results

Presentation is also extremely important, as project results should be clearly outlined for the convenience of stakeholders (who, in the vast majority of instances, are without technical knowledge). The data scientist has to possess the “gift” of storytelling so that the entire process makes sense, meeting the necessary requirements to solve the company’s problem.

If you want to know more about data science projects or if you’d like a bit of advice, don’t hesitate to get in touch.

Sílvia Raposo7 steps to implement a data science project
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The impact of Big Data on Social Media Marketing

Social media was born with the intent to create remote connections between colleagues, friends and others who wanted to share knowledge and information. Even though this purpose is still prevalent in its genesis, the truth is that social media has been evolving exponentially throughout the years, becoming a powerful bi-directional communication tool between companies and clients.

Nowadays, social media allows companies to publicise their brand and products, facilitating the rapid growth of their client base while also allowing the ceaseless collection of inputs from their users, whether they are clients or not.

For that reason, each like, comment or share gives companies a better understanding of their clients and their respective behaviours, through the way in which they interact with specific types of content. This behavioural analysis and exchange of information generates a massive volume of data, which can only be stored and processed using “Big Data” technologies.

In reality, Big Data has impacted on almost every sector of our daily lives, shaping the way people communicate, work and even have fun.

In recent decades, the quantity of generated data has been growing exponentially, doubling its size every two years, potentially reaching 44 trillion gigabytes in the year 2020. The massification of the World Wide Web and the Internet of things abruptly increased the amount of generated data, equally intensifying the necessity to diminish the time it takes to transform and access that same data.

Big Data is the technological concept that encompasses a particular set of practices and tools, tackling this problem using 5 fundamental principles:

  • Volume (storing, processing and accessing vast amounts of data)
  • Variety (cross-referencing data from various sources)
  • Speed (data access, treatment and processing speed)
  • Veracity (guarantee the veracity of information)
  • Value (usefulness of the information processed)

This “new” data access method and processing power has established a new paradigm within the marketing sector. Now it’s easier to analyse and identify trends, as well as possible cause and effect relationships to apply to marketing strategies. These types of analyses have become indispensable to companies for increasing the percentage of messages that actually reach the target, resulting in the growth of their ROI (return on investment).

How do we take advantage of Big Data in a marketing strategy?

The first step is to establish a relation between non-structured data, provided by social media, and already available data, such as your clients’ details. After completing this step, it will be easier to observe and analyse your clients’ actions, thus collecting important insights that will form a solid base for your future campaigns.

Now you can outline marketing strategies focused on all the insights you’ve gathered. In other words, you are now able to design marketing campaigns anchored by content that fulfills the needs of your clients, or segmented groups of clients.

Execution time has arrived! Now you possess the most actionable content, based on your analyses, let’s discover the degree of effectiveness of your strategy.

You’ve almost certainly worked out that this is a fundamental formula to success, but reaching that sweet spot will require constant “fine-tuning”. In other words, from this point forward, your digital marketing strategy will work in cycle: the number of insights about your clients and the reach and suitability of your strategies and content are proportionately higher, which in turn implies more insights.

Social media marketing is a tool that allows a company of any dimension and in any market to better understand its clients and work out the most effective strategies to shape its offers in order to satisfy the needs of its clients.

The truth: without Big Data, none of this would have been possible!

Sílvia RaposoThe impact of Big Data on Social Media Marketing
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Machine Learning: autonomous learning

Machine Learning has been developing further every day, thanks to the digital transformation movement. The original basis was a theory that believed computers could learn to perform specific tasks and to recognise patterns. The challenge was simple: to check if computers could learn from data.

Machine Learning provides systems with the possibility to learn and improve from experience, without needing specific programming for that effect. The focus is on developing programs that use available data and can learn on their own. The mathematical models are built and powered with – potentially – large amounts of data. The algorithms learn to identify patterns and to extract insights that are applied when new information is processed. This term dates back to 1959, when the pioneer Arthur Samuel defined Machine Learning as the ability of a computer to learn without being explicitly programmed to do so.

This learning process starts with data processing and trying to identify patterns. The main goal is to allow computers to learn autonomously without the need for human assistance, using that knowledge to make decisions according to what was “learnt”. Even though machine learning algorithms have been around for a long time, the application of these mathematical calculations to Big Data, with more frequency is a recent development. However, according to industry reports, what is considered to be an exponential growth in this area today is going to be seen as only “baby steps” in 50 years. This AI field is expected to grow extremely fast in the coming years.

Examples of Machine Learning

The continuing interest in this practice stems from a few key factors that have also made data mining and Bayesian analysis extremely popular: growth in the volume and variety of available data; cheaper and more powerful computational processes; and low cost storage.

A few examples of machine learning applications in some companies include self-driving vehicles; recommendations from online platforms such as Amazon and Netflix based on users’ behaviour; voice recognition systems such as SIRI and Cortana; PayPal’s platform, which is based on machine learning algorithms to fight fraud by analysing large quantities of data from the customer and assessing risks; the model from Uber that uses algorithms to determine time of arrival and departure locations; SPAM detecting mechanisms in email accounts; facial recognition that occurs in platforms such as Facebook.

Industries that are choosing Machine Learning

Most industries with large amounts of data have already acknowledged the potential of this technology. The possibility to extract insights allows companies to obtain a competitive advantage and work more efficiently.

Financial Services

Banks and other financial entities are using machine learning with two goals: extracting valuable insights from customer data and preventing fraud. Insights identify investment opportunities according to customers’ profiles, and, concerning fraud, the identification of high-risk customers and suspect transactions is improved.

Furthermore, this technology can also influence customer satisfaction. By analysing a user’s activity, smart machines can predict, for example, a possible account closure before it happens and prompt mitigating actions.

Health

Health entities can capitalise on the integration between IoT and data analysis to develop better solutions for patients. The emergence of wearables allows acquiring data related to the patients’ health, which, in turn, allows health professionals to detect relevant patterns including risk patterns. Therefore, this technology offers the potential for better diagnosis and treatment.

Retail

Nowadays, the impact of smart machines in users’ retail experience is quite obvious. The result is a highly personalised service that includes recommendations based on purchase history or online activity; improvements in customer service and delivery systems, where machines decipher the meaning of users’ emails and delivery notes, in order to prioritise tasks and ensure customer satisfaction; and dynamic price management by identifying patterns in price fluctuations and allowing to prices to be determined according to the demand. The ability to gather, analyse and use data to personalise, for example, a purchase experience (or implement a marketing campaign) is the future of retail.

Transportation

Analysing data to identify patterns and trends is key to the transportation industry, since profit growth means more efficient routes and the projection of potential problems. Data analysis and the modelling aspects of machine learning are important tools for delivery companies and public transportation, allowing them to improve their income.

Machine learning apps allow companies to automate the analysis and interpretation of business interactions, extracting valuable insights that make personalising products and services, possible.

Xpand IT has a complete service portfolio in Machine Learning. If you want to know how to use Machine Learning in your business and obtain real added value, we can help. Do you want to know how we can help your business? Contacts us here and get the best out of this technology!

Sílvia RaposoMachine Learning: autonomous learning
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DevOps is not Dev & Ops – What I didn’t know about DevOps

All these years I have heard about DevOps, but I was truly convinced it was too techy for me.

I thought it was about continuous integration, automation, and awesome DevOps guys, who knew not only how to develop software but also how to release and manage production environments…

Now, I realise that I was completely wrong… DevOps is not Dev & Ops teams together… but an entire organisation that collaborates – really collaborates.

Of course you need automation; of course you need continuous integration – but that’s not all.

In a DevOps culture you must follow these rules:

  • Know the flow = understand how work goes from “to do” to “done”
  • You don’t work in a silo = instead of working in an isolated team that is just worried about their “own” work, you work for a purpose/value
  • You are constantly learning & improving = Don’t waste time – if something needs to be changed, change it

But how can you transform a whole organisation? Below, you can see some practical tips:

  • Make your work visible to everyone; don’t worry what others may think about it.
  • Change your mindset. Let me tell you a story, that someone once told me:

JFK, once when visiting NASA, saw a janitor cleaning the floor and asked him: What are you doing? He expected an answer like “I am cleaning the floor”, but instead the man said “I’m helping the men get to the Moon.”

  • Add value to your user stories; don’t create them just for someone to do something, but because you need to generate value, like improving customer satisfaction to 80%.
  • Collaborate, collaborate, collaborate even more… No man is an island, so don’t work like one.

Tools are not the most important element, but they can definitely help. Running shoes don’t make you a runner, but they will help you to run better.

If you are searching for tools that can help you understand the flow of work, make your work visible, and help you collaborate better with your team, just take a look at Jira, which allows teams to capture and organise work, assign it to the team and track team activity.

Sofia Neto

Collaboration & Development Solutions Manager, Xpand IT

Sílvia RaposoDevOps is not Dev & Ops – What I didn’t know about DevOps
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Xpand IT receives Atlassian Philantropy Partner of the Year 2018

Barcelona, 4 September 2018 – “Moments like these are the ones that remind us that the path of giving back to the community can really make the change for a better world.” These were the words of Pedro Gonçalves, Xpand IT’s Co-Founder, after having received the award for Philanthropy Partner of The Year at the Atlassian Summit 2018.

Xpand IT was the first Portuguese company to join the Pledge 1% movement and by doing so, has committed to donate 1% of product and annual profit to charitable organisations. During the last year, it has been deeply involved with the philanthropic movement, developing a major series of initiatives aimed at giving back to the community.

Atlassian is thrilled to recognize and honor our 2018 Partner Award recipients“, said Martin Musierowicz, Atlassian’s Head of Global Channels. “Solution Partners are instrumental to our customers’ success and we are excited to be able to highlight some of our top partners who are going above and beyond to support customers and provide Atlassian services.

Xpand IT plans to raise the bar in disseminating the Pledge 1% mission in 2019 and multiply the number of initiatives aimed at helping those in need – all the while improving companies’ success through better collaboration using Atlassian technology.

Atlassian is the company behind products such as Jira, Jira Service Desk, Bitbucket and Confluence. Their mission is to help every team  unleash their potential. Xpand IT has achieved highest Solution Partner Level, Platinum, and has an impressive track-record of implementing projects based on Atlassian products.

For Sofia Neto, Collaboration & Development Solutions Lead at Xpand IT, being present at the Summit is not only a unique opportunity to meet people and share know-how and experiences, but is also recognition of the continuous hard work: “We’ve had the opportunity to participate in such exclusive events and the experience goes far beyond a traditional one. This is the second year in a row that we have been distinguished by Atlassian, this time recognising our involvement in the philanthropic movement Pledge 1%, and it’s something that truly makes us proud and just makes us want to do more and more. This is definitely a huge part of what it is to be an Xpander.”

Xpand IT team receives award from Mike Cannon-Brookes, the co-founder of Atlassian, the Philantropy Partner of the year 2018, in Barcelona.
Sílvia RaposoXpand IT receives Atlassian Philantropy Partner of the Year 2018
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