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.
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 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.
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.
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!
Superset is a modern BI app with a simple interface, feature-rich when it comes to views, that allows the user to create and share dashboards.
This app is simple and doesn’t require programming, and allows the user to explore, filter and organise data. The best part is… it’s Open Source!
What does Apache Superset provide?
What is truly appealing about Apache Superset is the fact that you can explore each dashboard in a complex way. Superset allows you to focus on each graph/metric and easily filter and organise.
Another attractive feature in this app is the SQL/IDE editor with interactive querying.
Concerning security, Superset allows you to define a list of users and a list of default functionalities (associated with the groups of users) and allows you to view user statistics, providing you total control. You can establish baseline permissions, as well as granting access to certain views or menus. The app also provides an action log.
Visually, Superset has a minimalist and well-organised interface. Even though it is not as easy to use as Tableau, Superset can be an alternative to creating dashboards or people with some knowledge of SQL.
Superset supports most SQL databases by using Python ORM (SQL Alchemy), which allows you to access MySQL, Postgres, Oracle, MS SQL Server, MariaDB, Sybase, Redshift and others (more information here).
Superset also works with Druid (for example, Airbnb uses Superset with Druid 0.8x), but it does not have all the advanced features available.
This feature is definitely a plus. SQL-Lab allows you to select a database, schema and table (previously uploaded) and do an interactive query, preview the data and also save the query history (as shown below).
A semantic layer allows you to define fields and metrics (for example, ratios or anything expressed by SQL):
You also have Python modules available (some available macros), inside SQL, via Jinja.
The least positive side of this is the fact that you cannot add or query multiple tables at the same time. The solution is making a view, which works as a logical layer and abstracts the query from SQL, therefore acting as a virtual table. The only negative aspect of this is that there will always be a query running against another view query, thereby potentially resulting in performance issues.
How to create a dashboard
To create a dashboard, Superset works as follows: there are sources, where you can find databases and tables; slices which are sheets with graphs; and, lastly, dashboards which are composed of groups of slices. Each slice is associated with one or more dashboards, and each dashboard has various associated slices.
Views have different types of graphs available such as histograms, box plots, heatmaps or line charts.
It is simple to edit graphs: the available features for each view are on the left-hand side, and you just have to change them and press “Run Query”.
Although flexible in most areas, Superset imposes some standardisation, which happens with the colour schemas.
Each view allows you to filter views through wildcards.
Superset also allows you to share the view, export data to .json and .csv, and see the exact query performed behind each view.
Superset integrates with the main authentication backends (database, OpenID, LDAP, OAuth, REMOTE_USE, …).
Concerning privileges, as stated above, this app provides default roles such as Admin (full access), Alpha Gamma, Sql_lab and Public.
It is possible to establish permissions for each user, restricting access to a subset of data sources, menus, views, specific metrics and other criteria. Hence, it is relatively easy to define which type of permission and/or access to data is granted to each person.
People using Superset
According to GitHub, Superset is currently being used by Airbnb, Twitter, GfK Data Lab, Yahoo!, Udemy and others.
It is important to note that “Superset was tested in large environments with hundreds of users. The production environment of Airbnb runs with Kubernetes and more than 600 active users who see more than 100 thousand graphs per day”.
Superset Vs Tableau
Able to join between tables within the same or different DBs.
Unable to query/join multiple tables. Only possible view by view, which means having multiple queries, thereby affecting performance.
Detailed customisation of dashboards, with legends, filters, tags, etc.
Limited customisation by type of view (however, creation of CSS templates is available).
Easy beginner learning and doesn’t require users to know SQL. Since the platform allows more complex and flexible tasks, there is a second learning curve for users who want to make the best use of Tableau.
Easy and smooth learning, but requires SQL knowledge from users.
Free and Open Source
Superset’s main advantages
Besides all the advantages already stated, one of the main features of Superset is… it’s Open Source Business Intelligence!
Provides BI without needing code (easy to use for those who are not programmers: you only need to know basic SQL);
Easy and quick setup;
Provides “SQL-Lab” that allows interactive querying;
A semantic layer that broadens the dashboard with ratios and other metrics (based on SQL);
Easy and attractive interactive view, that allows data exploration;
Satisfies the needs of most companies to allow simple data analysis.
The app still doesn’t support NoSQL databases;
Even though the number of users is growing, it still has little or no support;
Sometimes, SQL-Lab freezes in queries for large amounts of data;
Has a considerable number of other unsolved issues.
Data Scientist, Xpand IT
Susana SantosApache Superset Open Source BI: almost the alternative to Tableau
IT teams receive a wide variety of service requests from their clients, including requests for access to apps, software improvements, computer upgrades and new smartphones. These kinds of requests are known, according to ITIL, as “Service Requests”, and “Request Fulfilment” is the corresponding management process. A lot of service requests are recurring, and in order to obtain maximum efficiency, it is necessary to establish processes and procedures to be followed.
Request fulfilment – what is it?
Request fulfilment is the process followed by the Service Desk team and consists of fulfilling a request from a client. Its mission is to answer the request with the best quality support. In an organisation where a high number of service requests need to be managed, it is recommended that the management is done via a totally independent workflow for logging and handling requests.
What are the four main processes of IT?
Service request management – a formal request from a user for something that needs to be provided.
Incident management – a non-planned disruption of an IT service or a reduction of its quality – for example: “The website is down.”
Problem management – eliminate recurring incidents and minimise incidents that can be prevented – for example: “The reporting app problem is happening again.”
Change management – standardised method to control changes to the IT system in order to minimise its impact on services – for example: “The database upgrade is now complete.”
Prioritisation in service request management
To IT teams in organisations, service requests frequently exceed available capacity in terms of time and resources. IT service teams in big companies constantly answer business requests and, much of the time, they must prioritise: responding first to clients who need the most attention. However, clients complain that it is hard to work with IT because they do not respond, and that it takes too long to complete requests that they need for work. A service request management system makes this process really simple, since it gives people a “self-service” ability, provides them with answers based on suggestions from a knowledge base and streamlines the whole request fulfilment process, therefore delivering an excellent service. In the light of all these issues, there are a few things that IT service teams should prioritise.
Top five prioritisations to deliver an excellent IT service:
Client comes first – service desk teams can often be driven by supply instead of demand. Are you creating a service request catalogue with self-service features because you think it is good, or are you working directly with your clients, meeting their biggest needs? A lot of organisations have created a catalogue portal of service requests that has resulted in a very low usability. Learn from others’ mistakes, and create something based on demand, not on supply.
Focus on “popular” requests – service desk teams can start from a broad and superficial standpoint or from a narrow and deep one. Understand what will serve your organisation and your clients best. It is common practice to start with a sub-group of “popular” services and expand from there, based on usage and feedback. Try not to overload your team in the early stages, and remember that a failed launch will make it more difficult to have clients coming back for a second try.
Integrate knowledge – clients seek answers. Therefore, give them easy access to the knowledge base and redirect tickets to searchable items. Providing a self-service experience that your associates love is the first step to make the whole process easier and to make them ask again.
Centralise the self-service portal – clients are always looking for a single place where they can get help, so even if you develop the most powerful self-service system, it will be useless if it’s not easily found by users. Always try to centralise and increase value when they use the services you offer.
Streamline automation – providing highly functional and knowledge-centric service request management is an excellent first step, but you need to find ways to make your IT team deliver even more value to your clients through a self-service experience. Here, the power to be effective is found in automation. When you incorporate automation into your service desk functionality, you reduce the overall workload of your IT team by taking care of the most common and repetitive tasks.
Service request management process
Even though there are some variations in the way a service request is fulfilled, it is important to focus on how to leverage standardisation and improve the general quality and efficiency of the service. The schema below shows a simple service request fulfilment process, based on the recommendations from ITIL, that can be used as a starting point to adapt existing processes or establish new ones.
An overview of the service request fulfilment process:
A client requests support through their catalogue of services or via email.
The service desk team analyses the request according to the qualification and approval process.
A team member of the service desk works to fulfil the request, or forwards the request to someone who is able to complete it.
When the request is fulfilled, the service desk completes the ticket. The team member consults the client to ensure that the request was fulfilled as expected.
Eight tips to consider when defining service requirements
Start with the items most frequently requested and choose those that can be fulfilled in the fastest and easiest way. This will allow you to deliver immediate value to your clients and will allow the service desk team to learn as they build further elements of the service requests catalogue.
Record every dimension of service requests (date of request, approval process, fulfilment procedures, fulfilment team, “owners” of the process, SLAs, reporting, etc.) before you add them to the catalogue. This will allow the IT team to manage the requirements of the request better over time. This step is really important to the more complex requests that will evolve in the future.
Collect all necessary data to start the request processes, but do not overload your client with too many questions.
Standardise the approval process as much as you can. For example, every request for a new monitor is considered pre-approved and every request for software requires approval from the client’s superior.
Review the process and procedures of request fulfilment to identify which support teams are responsible for answering and if there are any specific requirements.
Accept that knowledge must be provided in the knowledge base when a request offer is eleased. The main goals of self-service are to give clients what they want faster and to redirect requests as much as possible. This way you will be able to answer questions through a simple FAQ; include this knowledge as part of the plan when you create a service request offer.
Review the Service Level Agreements (SLAs) to ensure that you have the right metrics and notifications properly defined, allowing requests to be fulfilled in a viable period of time.
Accept that reporting is necessary, so you can properly manage the whole lifecycle of a service request and the catalogue, in the long run.
Associação Crescerbem is a Private Social Solidarity Institution, founded in 2011, and its main goal is to help families of hospitalised children in economic deprivation. The association started in Dona Estefânia Hospital in Lisbon, where the headquarters still remain nearby. Meanwhile, the support has been extended to Santa Maria Hospital and Beatriz Ângelo Hospital.
The mission of Crescerbem is, specifically, to provide for families in a personalised manner, according to the specific needs of each case and helping them become more independent and autonomous. This way, follow-up does not happen only when a child is hospitalised, but also in the period after the medical discharge.
The association started offering home support and, through that, other needs started to be identified besides medical follow-up; from that point on, countless parallel projects came to life, such as the solidarity pantry (food baskets provided to families), the laundry service, and the solidarity pharmacy (which provides the necessary medication).
Despite the number of families helped and the number of existing cases, all the information was still offline. This means that it was impossible to access the necessary information without physically being in the association’s headquarters. Volunteers spent hours searching for case files and updating them, which resulted in a huge lack of visibility on the current state of each support case. Therefore, computerising all this information was critical. However, there was a problem: the lack of financial means to spend on technological solutions that could put an end to this problem.
It was at this stage that Donate IT – a community of volunteers who work with information technology – came into action through Sofia Neto, community volunteer and Collaboration & Development Solutions Lead from Xpand IT. Sofia managed to unite her work at Donate IT to the movement Pledge 1% – a movement which Xpand IT joined in 2017, thereby committing to annually donate 1% of profit and 1% of products to social solidarity institutions. Completely pro bono, Xpand IT made Jira Software available to Crescerbem (including implementation and support), thereby accomplishing Donate IT’s vision – helping to help others – and Pledge 1%’s vision.
Besides being extremely important to the computerisation of all documents of Crescerbem, this project proves that Jira Software has a lot more uses than software management. In practice, it can be adapted to any reality.
Inside the association, each family corresponds to an ‘issue’ in Jira. When support to a new family starts, a new issue is created and the type family is chosen. Each issue has the complete information that characterises each family: information including when the support started, how many children, which country the family is from and the contact details of the parents.
Everything is organised in different tabs in order to make viewing and editing of the information on each family easier, and all initiatives are considered subtasks: e.g. home support or medication provided. Therefore, a new registry of everything that happens has become an integral part of the process, and everyone with access permission can access that information anywhere.
Xpand IT is already working on the new release and it will have two goals: the first one is to introduce Confluence to further facilitate the sharing of information about tasks to be undertaken or families with open cases; the second one is to include Xporter, so that the association can easily export a report, for example, and to always be able to present it with the best formatting.
With this software, we have the ability to computerise all social processes and this means that access to all information related to the families is only a click away. What is the importance of this change? We will have more time. Time to begin expanding to other hospitals; time to help more families; and time to move forward with the idea of a social business, which will make Crescerbem self-sustaining.
Ana PaneiroPledge 1%: Supporting Associação Crescerbem with Jira Software
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.
Collaboration & Development Solutions Manager, Xpand IT
Sílvia RaposoDevOps is not Dev & Ops – What I didn’t know about DevOps
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