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Data & Analytics

  • Data Warehousing
  • Data Lake
  • Streaming Data
  • Analytics
  • Machine Learning / Artificial Intelligence

If you’re like most decision-makers in a competitive environment, you struggle to get a clearly-informed picture of things but need to back up your decisions with data. With opportunities and threats coming from all sides, at a faster and faster pace, it’s never been more important to drive your business with an accurate view of reality.

We know you’re facing at least one of these common data problems:

  1. No data – you have no data at all.
  2. Slow data – you have the data, but it isn’t in front of you when you need it.
  3. Siloed data – your data is fragmented across your business, and you spend more time trying to get it than making sense of it.
  4. Bad data – you have data in front of you, but you can’t trust it.

We don’t think data-driven decision making should be so hard to achieve. That’s why we created a dedicated Data and Analytics Practice to guide decision makers to take advantage of the incredible data and analytics tools available in the cloud. Each of those four common data problems are a thing of the past for our customers.

3 Steps to a data-driven transformation

Your transformation to becoming data-driven follows a simple process.

  1. Define a high-value metric that would be a game-changer for decision-making, if you had it. Name an executive to be the champion.
  2. Build a best-practices data system in the cloud. Be action-oriented and skip the design debate. The best practices path is well-traveled, and you need results.
  3. Put your finished system in front of the business and start making better decisions.

How we do it

Being data-driven is a mindset. Successful data-driven organizations embrace a culture that demands data has a seat at the table. Our approach is action-oriented so you have frequent wins to share with your team – early and often – building momentum and credibility.

Whether you want to build in AWS, Azure, or GCP, we have best-practices and secure engagements for your data initiatives.

  • Data warehousing – migrations from Netezza, SQL Server Data Warehouse, Teradata, Oracle, and others to Redshift, Big Query, Azure Synapse, or net-new workloads
  • Data lake – put all your enterprise data behind a single data catalog
  • Streaming data
  • Analytics
  • Machine learning / Artificial Intelligence
  • Governance
  • Security
  • Archive
  • GDPR / HIPAA / PCI

The benefits you’ll get include:

  • More confidence and credibility in decision-making
  • Reduced operational costs
  • Faster decision cycles
  • Drastically less ambiguity around your business situation

 

Packaged Service Offerings

DataOps Foundation

If you can’t see all your data in one place – if you can’t query all your important company tables without logging into multiple systems – then you are not going as fast as you could. Our DataOps foundation is a short, 1-2 month engagement that propels you into cloud best-practices for data analysis.

Learn More

Managed DataOps Foundation

Once your dataOps foundation is live, other business units will want to use it. You will want to add more data sources to get better insights, and you will want help optimizing ETL pipelines. Leave the details to us while you focus on creating business value. Our managed dataops foundation is a standard managed services offering, but with the added benefit of having a team of data engineers at the ready, moving your data estate forward at all times.

Data Warehouse Health Check and Optimization

Your Amazon Redshift or Azure Synapse data warehouse is not static or build-it-once and forget it. As you add new data sources and business needs change, your cluster should change too. Our rapid, yet thorough analysis process reveals opportunities for cost savings, speed improvements, and performance enhancements. We’ll also create the roadmap to make those improvements a reality.

Governance Master Plan

Your Data Catalog should be one of the most closely-guarded and trustworthy assets in your organization. Your data catalog is like a search engine for all your enterprise data sets – it tells you what you have, where it is, how to access it, and how sensitive it is. You also need to know how clean your data is. Can you trust it? Who or what is accessing it? This governance layer is supremely important but can be overlooked simply because building and maintaining it requires a unique skillset.  We create a simple, clear data catalog that tells you exactly what data your enterprise has and where it is. Then, we set up access rules and put systems in place to ensure the data catalog is 100% trustworthy at all times.

Data Warehouse Migration

We know that your data warehouse is the epicenter of enterprise operations, so this is not a trivial engagement. Most migrations take 6-12 months, and a large portion of that time is devoted to retiring technical debt – namely the hundreds of worthless user defined functions and stored procedures that were written but never removed. Lifting and shifting your data warehouse into the cloud results in high compute costs for continuing to run those functions. If you have large volumes of warm or cold data, you save significant costs by architecting your data warehouse along with a data lake, which stores data at a fraction of the cost.  We help you plan your migration, give you realistic cost scenarios, working within your budget, and perform the migration. We also teach your team how to manage your data warehouse and optimize its performance, or we can manage it for you.

Hadoop Migration

The vast majority of on-premise Hadoop users save significantly when moving to the cloud. This migration is complex and one of the few migrations that leaves you worse off if you perform only a lift-and-shift without re-architecting. We help you strategize and plan your Hadoop deployment in light of your broader data strategy.

Machine Learning Ops Foundation

Our approach to Machine Learning is insisting on clear, tangible business outcomes. We realize the power of machine learning is high, and we want you to get significant credibility in your organization from the outcomes of your machine learning project. We help you build a repeatable machine learning pipeline that is easy for your engineers to use, with clear processes for sampling data for exploration, collaborating among developers, selecting algorithms to put into practice, and deploying your model – all using our best practices approach to streamline these processes.

 Why 2nd Watch

Large enterprises partner with 2nd Watch for our ability to walk alongside them to deliver tailored and integrated migration and management solutions that holistically and proactively encompass the operating, financial, and technical requirements for scaling long-term use of public cloud. In the end, clients gain more leverage from the cloud with a lot less risk.