Agile Lab is a company born in 2014, when in a somewhat pioneering way the founders decided to embark on this adventure by leveraging their knowledge of platforms and big data. A bet that paid off, if we think that Agile Lab now has about 130 employees and has attracted the attention of a company of the caliber of Poste Italiane, which acquired 70% for 18 million euros in October 2022. His specialization is that of consulting for other companies, helping them to extract greater value from the data in their possession, leveraging above all on the concept of Data Mesh.

Agile lab is a collective of data engineering professionals“, explains Ugo Ciraci Agile Lab Business Unit Lead.”We are very focused on people, on their training and professional growth, so that they have and receive the trust and alignment to work independently and with quality for our customers”.

For companies”true ROI is difficult to establish [Return on Investment) sul dato, ma anche percepire il vantaggio competitivo che deriva da questo investimento”. Per questo motivo, le figure formate da Agile Lab sono ingegneri che lavorano presso i clienti e cercano di capire “come si muove l’organizzazione attorno al dato, sia dal punto di vista tecnologico sia da quello culturale. Trascurare l’aspetto organizzativo vuol dire mettere a rischio un intero progetto di trasformazione digitale”.

Comprendere come sono organizzate le informazioni, quali silos informativi sono presenti e come si muove il cliente nei confronti del dato è secondo Agile Lab fondamentale per poter dare un supporto concreto, ed è un aspetto non meno importante di quello tecnologico. “Architetture basate su data lake o data warehouse sono diventate poco scalabili nel momento in cui l’azienda è cresciuta o il mercato si è diversificato, perché non erano più sostenibili dal punto di vista organizzativo”. Ecco perché uno dei modelli su quali spinge Agile Lab (ma non l’unico) è quello del Data Mesh, un approccio socio-tecnico per decentralizzare la proprietà dei dati ai domini aziendali.

Alla scoperta del Data Mesh

Quando affrontiamo un’iniziativa Data Mesh lo facciamo partendo dall’aspetto organizzativo, perché la tecnologia da sola non può risolvere la complessità di un’azienda che vuole scalare il modo in cui gestisce i dati”. Ciracì fa un esempio concreto, quello di un’ipotetica grande azienda operante nel mondo delle utility, una realtà molto articolata, che opera da decenni e che ha stratificato nel tempo tecnologie, piattaforme, iniziative di data lake e data warehouse. Una situazione complessa, ma per mettere ordine la tecnologia non basta. “Queste aziende di tecnologia ne hanno a bizzeffe”, sottolinea Ciracì. Il problema è che poi ogni volta che è necessario estrarne il valore, è necessario coinvolgere svariate figure tecniche come data scientist e data engineer che li andranno a esportare, analizzare e passare già elaborati a chi ne ha bisogno in azienda. Creando spesso un collo di bottiglia.

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Data Mesh, on the other hand, “it is a non-technological, but a socio-technical paradigm”: data is in fact decentralized, so as to allow the various departments to access it more quickly, without having to bother engineers thanks to high automation and a self-service approach. In the data mesh, the data is treated in all respects as a product, and each department that uses the data becomes fully responsible for it. An approach that brings the following advantages: it ensures high quality and standardization of data access, great agility, speeding up the time to market of products and allows you to better manage complexity when the amount of information is gigantic.

agile datamesh lavb

These are the pillars on which the Data Mesh concept is based:

  • Decentralized domain oriented ownership: whoever generates the data is also responsible for the relative analytical part. A fundamental aspect, more organizational than technological;
  • Data as a product: manage the data as a product. Whoever handles the information must be responsible for its origin, quality and use. Also in this case it is more an organizational concept than a technology one, given that it is not the data platform that guarantees responsibility for the data, but the organizational approach. In fact, Ciracì explains that Agile Lab does not tend to propose additional data platforms, but to leverage those already implemented. To reduce time and costs, but also to avoid increasing complexity by simply creating new silos;
  • Building a self-service platformusable through standard interfaces, so that anyone in the company can use, having the rights, the data generated by each compartment.
  • Federated computational governance in which domains, data mesh platform teams and SMEs can decide evolutionary, architectural, infrastructural, organizational, standard and any other aspect that has an impact on the Data Mesh with the aim of automating (as much as possible) the application of these rules.

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What does Agile Lab do?

Ugo Ciraci_Agile LabAgile Lab helps its customers understand how to organize information and make it available in a simple, safe and effective way to all the figures in the company who have to deal with it. “Most of the activity we do is done by design and implementation. We have strong expertise on open source technology park related to big data; we are experts in systems such as Cloudera and Databricks and we enter into partnerships with these vendors and cloud providers (AWS, Azure, Google) to bring our knowledge closer to modernity, but we do not favor the technological aspect. We try to understand our customer’s problem and then propose a solution. Which is never just technological”.

Ciracì underlines that in Agile Lab’s vision, proposing a data platform as a solution to a Data Mesh problem is not a good service. “When we go to a customer we try to understand his technological portfolio, his technical culture and above all his business needs. Once these analyzes have been done, we can go on to build or even propose technological solutions. It is company policy that we never choose technologies to promote partnerships”, says Ciracì, underlining Agile Lab’s agnostic approach towards those who supply technologies. The company’s business is specialized data engineering consultancy, not the sale of third-party technology solutions for its own sake.

This does not mean that the company has not partnered with suppliers or does not offer proprietary solutions, but that it offers a specific technology to customers only if theirs “is the right one in the right case. There is no absolute best technology: it depends on the customer’s use cases”. In any case, Agile Lab’s philosophy is to move towards open standards, which simplify the management and processing of information and do not risk creating further complexity. Using non-open standards, in fact, the risk is that of having to invest time and resources to move data from one silo to another even just to be able to transform and process them, moving away from the agility which is instead one of the strengths of the Data Mesh paradigm.

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