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Integrate Data Silos with Azure Synapse Analytics

The Roadblock for Digital Transformation

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Source: Harvard Business Review

It is clearly established that Digital Transformation is the key to success and even survival for organizations, even more so with the current global crisis due to COVID-19. 64% of executives believe that they have less than four years to complete digital transformation or they will go out of business. 91% of global executives surveyed by Harvard Business Review feel that effective data and analytics strategies are essential for digital transformation. This data driven culture is critical to spark innovation and drive efficiencies, which is crucial for survival.

But, 80% of the respondents also say that their organizations are struggling to become mature users of data and analytics even though 79% of the employees use data and analytics at least once a week. What gets in the way of organizations effectively using data and analytics for business decisions?

More than half (55%) of the executives say the key roadblock stems from data silos and difficulty managing data coming from multiple systems. Digital transformation leads to a lot of data being captured across various systems which can be extremely valuable. However, less than 20% of this data can ever be analyzed due to the silos. This is mainly because of the disconnect between Big Data analytics, enterprise Data Warehousing, Analytics, and Artificial Intelligence/Machine Learning.

Simplifying Analytics

The need of the hour is to simplify analytics in a manner that breaks down these silos and makes the most of the data available for analysis without having to jump through hoops. In an ideal world, streaming operational data should be available for immediate analysis to generate reports and run models on the data. This is not a trivial problem.

Operational data is a mix of structured and unstructured data which is generally stored in a Data Lake, not suitable for analytics. Hence, the operational data needs to be imported into a Data Warehouse. The reporting and analytics services can then run on the Data Warehouse.

This creates three key issues. 

  1. Lag between the operational and analytics data stores due to the ELT pipeline. 
  2. Balancing the operational, ELT, reporting, and analytics workloads in the cloud. 
  3. Efficient and effective model management.

Organizations would really benefit from a framework which effectively addresses these issues and removes the roadblocks to data maturity. Azure Synapse Analytics is a step in the right direction with a big promise – Limitless Analytics Services in the cloud.

Azure Synapse Analytics to the Rescue

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Source: Microsoft

Microsoft has launched Azure Synapse Analytics to fulfill the promise of limitless analytics services. This service creates a single place to collaborate for Data Engineers, Database Administrators, Data Scientists, Business Intelligence Analysts, and Business Users with everyone accessing the same data.

The service offers a distributed query processing engine, versatile form factor for computing (cluster/ serverless), and a single experience for the users to manage the end-to-end process. This provides the much required flexibility in scaling and a great user experience, which promotes collaboration.

Many features of Azure Synapse Analytics are now generally available with many more in the pipeline. We believe that this service will evolve rapidly into the standard for analytics at scale for organizations.

Benefits of Azure Synapse Analytics

Azure Synapse Analytics allows teams to seamlessly work together. However, the benefits go beyond this. Some additional benefits are:

1. Unified Experience

 

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Source: Microsoft

Azure Synapse Analytics allows users to ingest, prepare, manage, serve, visualize, and analyse the data using a unified experience. Users can bring their analytics to where the data is located, rather than switching to a different interface. This gives a big boost to productivity.

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2. Limitless Scale

Azure Synapse Analytics enables limitless scaling for data and analytics in the cloud. Data professionals can derive insights from all the data across data warehouses and big analytics systems at speed. They can query both relational and non-relational data at petabyte-scale using T-SQL language. Furthermore, they can benefit from a versatile form factor of using clusters and serverless computing. Finally, they can run analytics systems along with mission critical workloads with intelligent workload management, workload isolation, and limitless concurrency.

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3. Integrate Business Intelligence and Machine Learning

Azure Synapse Analytics allows users to integrate Power BI and Azure Machine Learning within the Azure Synapse Studio. Then BI professionals and Data Scientists can tap into the available data immediately to create faster insights.

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4. Cloud-Native HTAP Implementation

The announcement of Azure Synapse Link (Preview) brings cloud-native hybrid transactional and analytical processing (HTAP) for Azure Cosmos DB. And with this, plans to expand it to other data stores in the future. It creates a tight seamless integration between Azure Cosmos DB and Azure Synapse Analytics. This enables users to run near real-time analytics over operational data which is stored in Azure Cosmos DB.

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Want to learn more? Click here for a quick and informative video that demonstrates the power of Synapse Analytics Link.

5. Price-Performance

Price-performance is also a critical part of data solutions. According to Microsoft, Azure Synapse Analytics offers better price-performance as compared to Google BigQuery and Amazon Redshift based on field tests done by GigaOm.

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Source: GigaOm Report

The TPC-H and TPC-DS results published by Microsoft show a significant reduction in price of Azure Synapse Analytics as compared to the others in the preceding as well as following graphics.

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Source: Microsoft

Speed

As demonstrated in this video from Ignite 2019, Azure Synapse Analytics can be blazing fast in a petabyte-scale environment combining relational and non-relational data. This can be a game changer for organizations where faster decision making can lead to substantial profit increase.

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Getting Started

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We have multiple offers that make it easy for organizations to get started with Azure Synapse Analytics no matter what stage they are at in the process. 

  1. Just getting started?  We offer a free two-hour lunch and learn workshop to help you understand this service. 
  2. Do you already know about the service but need help figuring out your next step?  We can conduct an assessment, strategy, and roadmap workshop that will provide your organization a plan with how to move forward. 
  3. Do you have a roadmap but need help with implementation? We can get you started with the first pilot which can be completed in 2-4 weeks. Once you have experienced the value from the pilot, we can help you with the implementation as per the roadmap.

 

Contact us at info@optimusinfo.com to get started.

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Starting a Data Project

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It’s exciting to hear ‘Data is the new Oil’ or the ‘new Gold’ or the new ‘something valuable’. What I dread, though, is the day we hear ‘Data is the new fad and a complete waste of money’. I hope that day never comes!

A lot will depend on how businesses approach data projects. Right now, it could go either way. There are many organizations throwing money at data projects to ensure they are not left behind. There are many more who are not even getting started fearing the outcome or the futility of it. If you belong to either camp, I will share a simple process to maximize the return on your data projects.

Where Data Projects fail

Data projects are complex and resource intensive and hence have many failure points. Most failure points are like the failure points of any complex project. Data availability, data quality, team quality, team work, communication, and so on. There is one, though, which is unique to data projects and at the root of all failed projects. It’s what I call the ‘rabbit hole question’. If a data project begins with this, it is likely to fail.

The Rabbit Hole Question

This is the question I most often hear from companies wanting to start data projects. It is some variation of – ‘What can I do with my data?’. I agree that it is the most natural question to ask, however, not the question that is going to set you up for success. It is the dream question for the salesperson who can now engage the solution architects. Who will then build an exciting solution. A solution that is likely to cost a lot of money and take a lot of time. Worse, it may not yield any results. Why? because it’s the ‘rabbit hole question’.

This question propels everyone to start thinking about what all to do with the data. Or, where all to apply the algorithm or the tool. There are many possibilities and hence many potential projects. But there is no way to figure out what we will get at the  end of these projects. We will only discover it as we go along. And chances are we may not like what we see in the end, if we see anything at all.

Avoiding the Trap

So, how do we avoid the ‘rabbit hole question’? Where do we start and how do we proceed to maximize our chance of success? The answer is to flip the question – ask “What can my data do for me?”. Better still, use a top down approach of starting with your Business Objectives. The graphic on the right illustrates a more sensible approach to data projects.

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The key is to break down the process into two phases – Planning and Execution. Planning requires little time but a lot of thinking but is crucial for success.

It is important during planning to stop thinking about the data you have and what to do it. Instead, start with what the key objectives for your business. Next is to think about the Actions required to achieve those objectives. That leads us to thinking about the kind of decisions we need to take. Then we can ask the question – “What insights do I need to take these decisions?”. These required insights then lead us to the relevant data and findings.

In this process, we may find that we do not have some of the required data. We can then start collecting those. In the meantime, we can then switch to execution with the data we already have. We can use the data and findings to generate relevant insights. These insights then drive the appropriate decisions. These decisions then guide us with the required actions to achieve our objectives.

Data Strategy Workshop

In our experience, the knowledge required for Planning is available in the organization. It usually sits in different silos though. Also, we find that the key stakeholders are usually not aligned.

Hence, we recommend conducting a Data Strategy workshop. Such a workshop aligns all stakeholders around the business objectives. It then allows the group to connect the objectives all the way to the Data they have.

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The outcome of the Workshop is an aligned Data & AI Roadmap. We can then jump into execution with the least effort and cost. The initial success then builds confidence in the organization for further projects. It also frees up time of critical resources to contribute to these projects.

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Optimus has already conducted Data & AI workshops for various organizations with fantastic results. If you would like your organization to have a clearly defined, cost effective, Data & AI Roadmap, please contact us at rajeev.roy@optimusinfo.com 

 

 

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