How we help our clients to improve analytics thanks to a Modern Data Platform?
17 May 2021
Key points:
- How to combine structured data required for operational reporting and raw data needed for deep analysis such as looking for trends?
- What are the best practices to build a data-driven organization ready for rapid change?
- How can you deploy the Modern Data Platform based on Microsoft technology stack?
Today’s business world is in constant change. This translates directly into IT solutions that are under increasing pressure to deliver and adapt solutions quickly. Analytics is an area that has undergone very dynamic and major changes in recent years. Solutions aimed at bringing the most business benefits must keep pace with expectations in terms of speed of implementation.
55% of organisations agreed that data analytics for decision-making is extremely important today, and 92% confirmed the increasingimportance of data and analytics through 2020 and 2021.
(Source: hbr-report-scaling-analytics-across-the-org.pdf |alteryx.com)
The digital transformation revolution is progressing, led by modern analytical solutions that must keep pace with it. ,,As part of the digital transformation in the organization, modern analytical methods are becoming the ultimate answer. Data insights can form the basis for decision-making or contribute to increasing the rate of return on marketing campaigns. Data is used to build a competitive advantage” We covered this topic in the previous article in the series, which you can read here.
As long as I remember, the answer to the analytical and reporting needs in the organization was a Data Warehouse implemented on on-premises infrastructure. Considering the time needed to implement, it largely depended on purchase and configuration of the infrastructure. In addition, a lot of it work had to be spent on requirements analysis, modeling, testing, and implementation (that has not changed). Quite often it turned out that after implementation the solution was outdated in comparison to current business needs, and its adaptation required going through the solution delivery cycle again.
Nowadays, an analyst describing what happened is no longer enough. We need to know why this happened, and preferably what will happen in the future. To do this, in addition to classic reporting, we need to use advanced analytics for example machine learning algorithms used for prediction or forecasting specific values like the number of customers.
For the needs of advanced analyzes, the Data Warehouse due to its stability is not enough flexible, and it sIts limitations are unacceptable in the world of Data Science. Data Science needs data as soon as possible, in as unprocessed form as possible., structured data available in analytical models is an unnecessary limitation.
The Modern Data Platform is a new approach to building analytical environments
These challenges are met by Modern Data Platforms, which combine the functionalities of an organized Data Warehouse, and at the same time provide Advanced Analysts with access to raw data. In one technology stack it allows to perform analytical activities on modern services inter-connected platform. Thanks to this, we can reduce the silos of solutions and have a true, which we all strive for in every organization, one source of truth.
Analytical solutions have become the flagship products in the offer of cloud services, the providers of which are outdoing each other in providing more and more innovative solutions. Microsoft with its Azure Synapse Analytics competes with Snowflake, Google BigQuery or Amazon Athena. The new solutions take full advantage of the best features of Cloud computing and enable the delivery of the required functionalities much faster than before.
A change of approach would not be possible without cloud platforms such as Microsoft Azure. Cloud platforms enabled a revolution in the approach to delivering solutions and managing their costs. A Modern Data Platform built on a cloud platform offers plenty of benefits – primarily: speed of implementation, cost-effectiveness and flexibility:
Organizations implementing Modern Data Platforms can expect reducing the total cost of ownership (TCO), by reducing the time needed to deliver a solution and increasing the productivity of your analysts. Thanks to the tools, analysts spend their time transferring and preparing data. Eventually, The business personas can spend more time on innovative applications use of analytical tools.
“… despite the rise in the value of data analytics in the current era, many organizations are burdened with outdated processes. According to IDC, 70% of an analyst’s time is spent searching for data, and 44% of data workers’ time results in unsuccessful attempts. Together with the lack of talented professionals to harness the true power of data, these struggles further refrain business leaders from creating a modern analytic environment for their organization. “
(Source: Accelerating Digital Transformation with Data, People and Technologies | analyticsindiamag.com)
Cloud platforms offers Data Lake services for storing large volumes of data which are publicly available on cloud platforms. They enable data storage at a relatively low cost compared to on-premises solutions. For example, Azure Data Lake is a data layer called to store raw, unprocessed data, which, thanks to its low price, we can supply with all the data generated by our business. Of course, this is quite an extravagant approach, but if we are able to calculate what profits can be gained from for example knowledge about the client customer versus how much we pay for data storage – it is quite easy to calculate whether it pays off or not.
What does the Modern Analytical Data Platform consist of?
Diagram 1. Modern Data Warehouse concept
Data Staging & Data Lake storage
Data Lake enables you to organize and structure your data, creating a space that can serve as your first analytical layer. Of course, to use it, we must have the right tools and competences. For e.g., Data Scientists view it as an ideal source of data, unmodified for the purposes of reports. Thanks to this approach, we centralize all analytical resources in the company on one repository and we can avoid building multiple copies of the same information, i.e., information silos.
Additionally, Data Lake is a data source for the analytical layer where data is structured to prepare business analyzes. We can feed data into structured analytical models, such as Azure Synapse Analytics, which is a unified environment for obtaining, exploring, preparing, managing and sharing data for immediate business analysis and machine learning.
Data Warehouse
Azure Synapse Analytics is a modern data warehouse of MPP (Massive Parallel Processing) class connected with serverless query service, which is Microsoft’s answer to the needs of fast data ordering, quick accesses and efficient queries. It is an unlimited analytics service that combines data integration, enterprise data warehousing, and big data analytics. Can be used as a kind of data virtualization layer for Data Lake as it allows querying file-based data in a way known from relational databases. It allows to apply table structures onto the files dynamically. This provides users the flexibility of work using common interfaces such as SQL while accessing data stored in files.
Additionally, it can be both a working environment for Data Engineers and Data Scientists. It has solutions that enable work both for people who prefer classic analytics based on the SQL language, but also meets the support of Python or Scala languages by offering a choice of a computing engine between the SQL engine and Spark.
To summarize, it is the heart of an analytical platform that is designed to provide standardized data for reporting and analysis.
Data Modelling & Information Access
Power BI is a great tool for presenting the information. It is incomparable in data visualization and is one of the best reporting tools on the market.
Power BI is an ideal environment for the implementation of Proof-of-Concept projects. It has a set of tools to collect, clean and transform data. At the same time, it is the leading tool in visualization. Using it, we can verify the previous assumptions in just a few days from A to Z.
Just imagine the time and resources we can save by verifying the concepts without the need to transfer data through the layers in the Data Warehouse. However, in a scenario with Data Lake and Data Warehouse included it can be a great layer for data visualization & information access.
Thanks to its features that allow to create data insights and many different data sources this is the best tool known for the elasticity of creating reporting and self-service data insights for analysts.
Data Transformation & Machine Learning
The element that binds the above components together, emphasizing that analytical platforms have changed radically, are tools that support and use Artificial Intelligence. Today we can implement machine learning algorithms in practically every component.
Thanks to services on Azure, such as Azure DataBricks, which is very often used to transform and enrich data with algorithm results. An alternative from Microsoft is Azure Machine Learning service, which is a comprehensive work environment for advanced analysts. It allows you to work on data directly from the Data Lake layer and share the results anywhere in the ecosystem of the analytical platform.
“Transformations are becoming commonplace across industries. Survey participants completed an average of six transformations in the past three years, with a range of objectives. More than 80 percent have implemented at least one end-to-end customer journey transformation, and 70 percent developed new digital propositions and ecosystems.”
(Source: Derisking digital and analytics transformations | McKinsey)
The expansion of tools based on artificial intelligence means that often people dealing with implementations start to wonder which component to use and when. We also face such challenges in our projects. We always try to look from the perspective of the client and his needs. Azure Synapse Analytics, Azure Machine Learning or Azure Databricks have many common features, but there are also many elements that distinguish them.
How to approach it?
It’s easy to get lost in the flood of new services and rapidly changing components. In addition, the ever-growing amount of operational works and the lack of time make it impossible to keep up with the news. That is why it is worth trusting experts who, based on their experience and accumulated knowledge, will advise the best solution for specific needs.
In the article, I have barely scratched the individual components to give you a landscape of available solutions. I am aware that we are dealing with a very wide topic, a more advanced description of which would fill several books.
Recently, we met the challenge of designing the appropriate architecture at one of our clients. The client struggled with typical challenges for a large corporation. How to build a flexible analytical platform that will prevent the formation of data silos. At the same time, it will enable standardization and control over data and the way it is shared. Additionally, it will enable the implementation and development of self-service. An important element is opening the entire environment to advanced analytics by, for example, building machine learning models that learn from raw data stored in Data Lake.
An important issue was the location of the real-time data stream reporting capability in the architecture, which is why the architecture includes appropriate components that can handle such reporting.
We offered the client the universal architecture of the Modern Data Platform, which addressed all his challenges.
At Elitmind, we carry out both Data Platform implementation projects and support our clients in developing the best analytical approach strategy tailored to their needs and the architecture of the analytical environment enabling both operational reporting on the company’s current operations, the use of Artificial Intelligence for more advanced analyzes, as well as a work environment for Data Scientists who do not need prepared reports, only access to a data set (preferably unprocessed).
Key takeaways
- The existing architectures of analytical systems no longer meet the requirements for the speed of solution delivery and the ability to meet business needs. The key to success is the agility and flexibility of implementation, not only in terms of project management but also in terms of delivering the solution to the client. Good implementation can be the foundation for the future success of the company, constituting a key tool enabling building a competitive advantage.
- Analytical solutions have become the flagship products in the offer of cloud services, the providers of which are outdoing each other in providing modern solutions. Microsoft with its Synapse Analytics competes with Snowflake, Google BigQuery, or Amazon Athena. The new solutions take full advantage of the best features of Cloud computing and enable the delivery of the required functionalities much faster than before.
- A change of approach would not be possible without cloud platforms such as Microsoft Azure. Cloud platforms enable a revolution in the approach to delivering solutions and costs. The combination of the Modern Data Warehouse architecture built on a cloud platform offers several benefits. These benefits are primarily: speed of implementation, cost-effectiveness and flexibility:
- At Elitmind, we carry out both Analytical Platform implementation projects and support our clients in developing the best analytical approach strategy tailored to their needs. We deliver and the architecture of the analytical environment enabling both operational reporting on the company’s current operations, the use of Artificial Intelligence for more advanced analyzes, as well as a work environment for Data Scientists who do not need prepared reports, only access to a data set (preferably unprocessed).
Do you want to learn more about the possibilities and benefits that Modern Data Platform can offer your organization? Visit our Modern Data Platform section!
- Elitmind is a leading company that implements analytical projects based on the cloud. We work with companies that have built or want to build a work culture based on data values. What counts for our clients is not only the final product but also the speed of implementation and flexibility. With the help of a staff of highly qualified experts, we carry out implementation projects by providing analytical tools and data platforms. We provide services for companies that want to build their competitive advantage based on data. We are data enthusiasts and enthusiasts of cloud technologies. We believe that the combination of these two elements allows us to provide the best solutions at the right time.
Did you like our article? Check part 1 of Modern Data Warehouse series