data management Archives

3 Data Priorities for Organic Value Creation

Organic value creation focuses on a few main areas, including improving current performance (both financial and operational) of your companies, establishing a pattern of consistent growth, strengthening your organizational leadership team, and building the potential for a brighter future through product and competitive positioning. All of these are supported by and/or partially based on the data foundation you create in your companies. At exit, your buyers want to see and feel confident that the created organic value is sustainable and will endure. Data and analytics are key to proving that.

Companies that solely focus on competition will ultimately die. Those that focus on value creation will thrive. — Edward De Bono

To organically create and drive value, there are a few key data priorities you should consider:

A starting point is data quality, which underpins all you will ever do and achieve with data in your organization. Achieving better-quality data is an unrelenting task, one that many organizations overlook.
Data monetization is a second priority and is also not top-of-mind for many organizations. The adage that “data is the new oil” is at least partially true, and most companies have ways and means to leverage the data they already possess to monetize and grow revenue for improved financial returns.
A third data priority is to focus on user adoption. Having ready data and elite-level analytical tools is not sufficient. You need to be sure the data and tools you have invested in are broadly used – and not just in the short term. You also need to continue to evolve and enhance both your data and your tools to grow that adoption for future success.

Data Quality

Data quality is a complicated topic worthy of a separate article. Let’s focus our data quality discussion on two things: trust and the process of data quality.

If you are organically growing your companies and increasing the use of and reliance upon your data, you better make sure you trust your data. The future of your analytics solutions and broad adoption across your operational management teams depend on your data being trustworthy. That trust means that the data is accurate, consistent across the organization, timely, and involved in a process to ensure the continuing trust in the data. There is also an assumption that your data aligns with external data sources. You can measure accuracy of your portfolio company’s data in many ways, but the single best measure is going to be how your operating executives answer the question, “How much do you trust your data?”

Data quality is never stagnant. There are always new data sources, changes in the data itself, outside influences on the data, etc. You cannot just clean the data once and expect it to stay clean. The best analogy is a stream that can get polluted from any source that feeds into the stream. To maintain high data quality over time, you need to build and incorporate processes and organizational structures that monitor, manage, and own the quality of your company’s data.

One “buzzwordy” term often applied to good data governance is data stewardship – the idea being that someone within your enterprise has the authority and responsibility to keep your data of the highest quality. There are efficient and effective ways to dramatically improve your company data and to keep it of the highest quality as you grow the organization. Simply put, do something about data quality, make sure that someone or some group is responsible for data quality, and find ways to measure your overall data quality over time.

A leading equipment distributor found new revenue sources and increased competitive edge by leveraging the cloud data warehouse that 2nd Watch built for their growing company to share data on parts availability in their industry. Using the centralized data, they can grow revenue, increase customer service levels, and have more industry leverage from data that they already owned. Read this private equity case study here.

Data Monetization

Organic value creation can also come from creating value out of the data your portfolio companies already own. Data monetization for you can mean such options as:

Enriching your internal data – Seek ways to make your data more valuable internally. This most often comes from cross-functional data creation (e.g., taking costing data and marrying it with sales/marketing data to infer lifetime customer value). The unique view that this enriched internal data offers will often lead to better internal decision-making and will drive more profitable analytics as you grow your analytics solutions library.

Finding private value buyers – Your data, cleansed and anonymized, is highly valuable. Your suppliers will pay for access to more data and information that helps them customize their offerings and prices to create value for customers. Your own customers would pay for enhanced information about your products and services if you can add value to them in the process. Within your industry, there are many ways to anonymize and sell the data that your portfolio companies create.

Finding public value buyers – Industry trade associations, consultancies, conference organizations, and the leading advisory firms are all eager to access unique insights and statistics they can use and sell to their own clients to generate competitive advantage.

Building a data factory mindset – Modern cloud data warehouse solutions make the technology to monetize your data quite easy. There are simple ways to make the data accessible and a marketplace for selling such data from each of the major cloud data warehouse vendors. The hardest part is not finding buyers or getting them the data; it is building an internal mindset that your internal data is a valuable asset that can be easily monetized.

User Adoption

Our firm works with many private equity clients to design, build, and implement leading analytics solutions. A consistent learning across our project work is that user adoption is a critical success factor in our work.

Just because we have more accurate data, or more timely data, or more enriched data won’t necessarily increase the adoption of advanced analytical solutions in your portfolio companies. Not all of your operating executives are data driven nor are they all analytically driven. Just because they capably produce their monthly reporting package and get it to you on time does not mean they are acting on issues and opportunities that they should be able to discern from the data. Better training, organizational change techniques, internal data sharing, and many other ways can dramatically increase the speed and depth of the user adoption in your companies.

You know how to seek value when you invest. You know how to grow your companies post-close. Growing organically during your hold period will drive increased exit valuations and let you outperform your investment thesis. Focus on data quality and broad user adoption as two of your analytics priorities for strong organic value creation across your portfolio.

Contact us today to set up a complimentary private equity data whiteboarding session. Our analytics experts have a template for data monetization and data quality assessments that we can run through with you and your team.

What Is the Difference Between Snowflake and Amazon Redshift?

The modern business world is data-centric. As more businesses turn to cloud computing, they must evaluate and choose the right data warehouse to support their digital modernization efforts and business outcomes. Data warehouses can increase the bottom line, improve analytics, enhance the customer experience, and optimize decision-making.

A data warehouse is a large repository of data businesses utilize for deep analytical insights and business intelligence. This data is collected from multiple data sources. A high-performing data warehouse can collect data from different operational databases and apply a uniform format for better analysis and quicker insights.

Two of the most popular data warehouse solutions are Snowflake and Amazon Web Services (AWS) Redshift. Let’s look at how these two data warehouses stack up against one another.

What is Snowflake?

Snowflake is a cloud-based data warehousing solution that uses third-party cloud-compute resources, such as Azure, Google Cloud Platform, or Amazon Web Services (AWS.) It is designed to provide users with a fully managed, cloud-native database solution that can scale up or down as needed for different workloads. Snowflake separates compute from storage: a non-traditional approach to data warehousing. With this method, data remains in a central repository while compute instances are managed, sized, and scaled independently.

Snowflake is a good choice for companies that are conscious about their operational overhead and need to quickly deploy applications into production without worrying about managing hardware or software. It is also the ideal platform to use when query loads are lighter, and the workload requires frequent scaling.

The benefits of Snowflake include:

Easy integration with most components of data ecosystems
Minimal operational overhead: companies are not responsible for installing, configuring, or managing the underlying warehouse platform
Simple setup and use
Abstracted configuration for storage and compute instances
Robust and intuitive SQL interface

What is Amazon Redshift?

Amazon Redshift is an enterprise data warehouse built on Amazon Web Services (AWS). It provides organizations with a scalable, secure, and cost-effective way to store and analyze large amounts of data in the cloud. Its cloud-based compute nodes enable businesses to perform large-scale data analysis and storage.

Amazon Redshift is ideal for enterprises that require quick query outputs on large data sets. Additionally, Redshift has several options for efficiently managing its clusters using AWS CLI/Amazon Redshift Console, Amazon Redshift Query API, and AWS Software Development Kit. Redshift is a great solution for companies already using AWS services and running applications with a high query load.

The benefits of Amazon Redshift include:

Seamless integration with the AWS ecosystem
Multiple data output formatting support
Easy console to extract analytics and run queries
Customizable data and security models

Comparing Data Warehouse Solutions

Snowflake and Amazon Redshift both offer impressive performance capabilities, like scalability across multiple servers and high availability with minimal downtime. There are some differences between the two that will determine which one is the best fit for your business.

Performance

Both data warehouse solutions harness massively parallel processing (MPP) and columnar storage, which enables advanced analytics and efficiency on massive jobs. Snowflake boasts a unique architecture that supports structured and semi-structured data. Storage, computing, and cloud services are abstracted to optimize independent performance. Redshift recently unveiled concurrency scaling coupled with machine learning to compete with Snowflake’s concurrency scaling.

Maintenance

Snowflake is a pure SaaS platform that doesn’t require any maintenance. All software and hardware maintenance is handled by Snowflake. Amazon Redshift’s clusters require manual maintenance from the user.

Data and Security Customization

Snowflake supports fewer customization choices in data and security. Snowflake’s security utilizes always-on encryption enforcing strict security checks. Redshift supports data flexibility via partitioning and distribution. Additionally, Redshift allows you to tailor its end-to-end encryption and set up your own identity management system to manage user authentication and authorization.

Pricing

Both platforms offer on-demand pricing but are packaged differently. Snowflake doesn’t bundle usage and storage in its pricing structure and treats them as separate entities. Redshift bundles the two in its pricing. Snowflake tiers its pricing based on what features you need. Your company can select a tier that best fits your feature needs. Redshift rewards businesses with discounts when they commit to longer-term contracts.

Which data warehouse is best for my business?

To determine the best fit for your business, ask yourself the following questions in these specific areas:

Do I want to bundle my features? Snowflake splits compute and storage, and its tiered pricing provides more flexibility to your business to purchase only the features you require. Redshift bundles compute and storage to unlock the immediate potential to scale for enterprise data warehouses.
Do I want a customizable security model? Snowflake grants security and compliance options geared toward each tier, so your company’s level of protection is relevant to your data strategy. Redshift provides fully customizable encryption solutions, so you can build a highly tailored security model.
Do I need JSON storage? Snowflake’s JSON storage support wins over Redshift’s support. With Snowflake, you can store and query JSON with native functions. With Redshift, JSON is split into strings, making it difficult to query and work with.
Do I need more automation? Snowflake automates issues like data vacuuming and compression. Redshift requires hands-on maintenance for these sorts of tasks.

Conclusion

A data warehouse is necessary to stay competitive in the modern business world. The two major data warehouse players – Snowflake and Amazon Redshift – are both best-in-class solutions. One product is not superior to the other, so choosing the right one for your business means identifying the one best for your data strategy.

2nd Watch is an AWS Certified Partner and an Elite Snowflake Consulting Partner. We can help you choose the right data warehouse solution for you and support your business regardless of which data warehouse your choose.

We have been recognized by AWS as a Premier Partner since 2012, as well as an audited and approved Managed Service Provider and Data and Analytics Competency partner for our outstanding customer experiences, depth and breadth of our products and services, and our ability to scale to meet customer demand. Our engineers and architects are 100% certified on AWS, holding more than 200 AWS certifications.

Our full team of certified SnowPros has proven expertise to help businesses implement modern data solutions using Snowflake. From creating a simple proof of concept to developing an enterprise data warehouse to customized Snowflake training programs, 2nd Watch will help you to utilize Snowflake’s powerful cloud-based data warehouse for all of your data needs.

Contact 2nd Watch today to help you choose the right data warehouse for your business!

4 Data Principles for Operational Resilience

Scaling your portfolio companies creates value, and increasing their native agility multiplies the value created. The foundation of better resilience in any company is often based on the ready availability of operational data. Access to the data you need to address problems or opportunities is necessary if you expect your operating executives and management teams to run the business more effectively than their competitors.

Resilience is the strength and speed of our response to adversity – and we can build it. It isn’t about having a backbone. It’s about strengthening the muscles around our backbone. — Sheryl Sandberg

You need and want your portfolio companies to be operationally resilient – to be ready and able to respond to changes and challenges in their operations. We all have seen dramatic market changes in recent years, and we all should expect continued dynamic economic and competitive pressures to challenge even the best of our portfolio companies. Resilient companies will respond better to such challenges and will outperform their peers.

This post highlights four areas that you and your operating executives should consider as you strive to make yourself more operationally resilient:

Data engineering takes time and effort. You can do a quick and dirty version of data engineering, also called loading it into a spreadsheet, but that won’t be sufficient to achieve what you really need in your companies.
Building a data-driven culture takes time. Having the data ready is not enough, you need to change the way your companies use the data in their tactical and strategic decision-making. And that takes some planning and some patience to achieve.
Adding value to the data takes time. Once you have easily accessible data, as an organization you should strive to add or enrich the data. Scoring customers or products, cleaning or scrubbing your source data, and adding external data are examples of ways you can enrich your data once you have it in a centrally accessible place.
Get after it. You need and want better analytics in every company you own or manage. This is a journey, not a single project. Getting started now is paramount to building agility and resiliency over time on that journey.

Data Engineering can be Laborious

Every company has multiple application source systems that generate and store data. Those multiple systems store the data in their proprietary databases, in a format that best suits transactional systems, and likely redundantly stores common reference data like customer number and customer name, address, etc. To get all that data, standardize it, scrub it, and model it in the way that you need to manage your business takes months. You likely must hire consultants to build the data pipelines, create a data warehouse to store the data, and then build the reports and dashboards for data analysis.

On most of our enterprise analytics projects, data engineering consumes 60-70% of the time and effort put into the project. Ask any financial analyst or business intelligence developer – most of their time is spent getting their hands on the right, clean data. Dashboards and reports are quickly built once the data is available.

CASE STUDY

The CEO of a large manufacturing company wanted to radically increase the level of data-driven decision-making in his company. Working with his executive team, we quickly realized that functional silos, prior lack of easy data access, and ingrained business processes were major inhibitors to achieving their vision. 2nd Watch incorporated extensive organizational change work while we built a new cloud-based analytics warehouse to facilitate and speed the pace of change. Read the full case study.

A Data-driven Culture needs to be Nurtured and Built

Giving your executives access to data and reports is only half the battle. Most executives are used to making decisions without the complete picture and without a full set of data. Resiliency comes from having the data and from using it wisely. If you build it, not all will come to use it.

Successful analytics projects incorporate organizational change management elements to drive better data behaviors. Training, better analytics tools, collaboration, and measuring adoption are just some of the best practices that you can bring to your analytics projects to drive better use of the data and analysis tools that will lead to more resilience in your portfolio companies.

Data Collaboration Increases the Value of your Data

We consistently find that cross-functional sharing of data and analytics increases the value and effectiveness of your decision-making. Most departments and functions have access to their own data – finance has access to the GL and financial data, marketing has access to marketing data, etc. Building a single data model that incorporates all of the data, from all of the silos, increases the level of collaboration that lets your executives from all functions simultaneously see and react to the performance of the business.

Let’s be honest, most enterprises are still managed through elaborate functional spreadsheets that serve as the best data source for quick analysis. Spreadsheets are fine for individual analysis and reporting, and for quick ad-hoc analytics. They are not a viable tool for extensive collaboration and won’t ever enable the data value enhancement that comes from a “single source of truth.”

Operating Executives need to Build Resilience as they Scale their Companies.

Change is constant, markets evolve, and today’s problems and opportunities are not tomorrow’s problems and opportunities. Modern data and analytics solutions can radically improve their operational resilience and drive higher value. These solutions can be technically and organizationally complex and will take time to implement and achieve results. Start building resiliency in your portfolio companies by mapping out a data strategy and creating the data foundation that your companies need.

Contact us today to set up a complimentary whiteboarding session. Our analytics experts will work through a high-level assessment with you.

How a Dedicated Data Warehouse Yields Better Insight than Your CRM or ERP

What percent of your enterprise data goes completely untapped? It’s far more than most organizations realize. Research suggests that as much as 68% of global enterprise data goes unused. The reasons are varied (we can get to the root cause with a current state assessment), but one growing problem stems from misconceptions about CRMs, ERPs, EHRs, and similar operational software systems.

The right operational software systems are valuable tools with their own effective reporting functions. The foundation of any successful reporting or analytics initiative depends on two factors: on a centralized source of truth that exists in a unified source format. All operational software systems struggle to satisfy either aspect of that criteria.

Believe it or not, one of the most strategic systems for data-driven decision-making is still a dedicated data warehouse. Here is the value a data warehouse brings to your organization and the necessary steps to implement that enhance your analytics’ accuracy and insight.

Download Now: Modern Data Warehouse Comparison Guide [Snowflake, Redshift, Azure Synapse, and Google BigQuery]

CRMs and ERPs Are Data Silos with Disparate Formats

Operational software systems are often advertised as offering a unified view, but that’s only true for their designed purpose. CRMs offer a comprehensive view of customers, ERPs of operations, and EHRs of patient or member medical history. Outside of their defined parameters, these systems are data silos.

In an HBR blog post, Edd Wilder-James captures the conundrum perfectly: “You can’t cleanly separate the data from its intended use. Depending on your desired application, you need to format, filter, and manipulate the data accordingly.”

Some platforms are enabled to integrate outside data sources, but even that provides you with a filtered view of your data, not the raw and centralized view necessary to generate granular and impactful reports. It’s the difference between abridged and unabridged books – you might glean chunks of the big picture but miss entire sections or chapters that are crucial to the overall story.

Building a dedicated data warehouse removes the question of whether your data sets are complete. You can extract, transfer, and load data from source systems into star schemas with a unified format optimized for business users to leverage. The data is formatted around the business process rather than the limitations of the tool. That way, you can run multifaceted reports or conduct advanced analytics when you need it – without anchoring yourself to any specific technology.

Tracking Down Your Data Sources

In all honesty, organizations not familiar with the process often overlook vital information sources. There might be a platform used to track shipping that only one member of your team uses. Maybe there’s a customer service representative who logs feedback in an ad hoc document. Or it’s possible there’s HIPAA-compliant software in use that isn’t automatically loading into your EHR. Regardless of your industry, there are likely gaps in your knowledge well outside of the CRMs, ERPs, EHRs, and other ostensibly complete data sources.

How do you build a single source of truth? It’s not as simple as shifting around a few sources. Implementing a dedicated data warehouse requires extensive planning and preparation. The journey starts with finding the invisible web of sources outside of your primary operational software systems. Those organizations that choose to forgo a full-fledged current state assessment to identify those hidden sources only achieve fragmentary analytics at best.

Data warehouse implementations need guidance and buy-in at the corporate level. That starts with a well-defined enterprise data strategy. Before you can create your strategy, you need to ask yourself questions such as these:

What are your primary business objectives?
What are your key performance indicators?
Which source systems contribute to those goals?
Which source systems are we currently using across the enterprise?

By obtaining the answers to these and other questions from decision-makers and end users, you can clarify the totality of your current state. Otherwise, hunting down those sources is an uphill battle.

Creating Data Warehouse Value that Lasts

Consolidating your dispersed data sources is just a starting point. Next, you need to extract the data from each source system and populate them within the data warehouse framework itself. A key component of this step is to test data within your warehouse to verify quality and completeness.

If data loss occurs during the ETL process, the impact of your work and veracity of your insights will be at risk. Running a variety of different tests (e.g., data accuracy, data completeness, data transformation, etc.) will reduce the possibility of any unanticipated biases in your single source of truth.

What about maintaining a healthy and dynamic data warehouse? How often should you load new data? The answer depends on the frequency of your reporting needs. As a rule of thumb, think in terms of freshness. If your data has gone stale by the time you’re loading it into your data warehouse, increase the frequency of your data refresh. Opt for real-time analytics if it will provide you with a strategic advantage, not because you want to keep current with the latest buzzword.

Improving Your Results with an Outsourced Partner

Each step in the process comes with its own complications. It’s easy to fall into common data warehousing pitfalls unless you have internal resources with experience pinpointing hidden data sources, selecting the right data model, and maintaining your data warehouse post-implementation.

One of our clients in the healthcare software space was struggling to transition to a dynamic data warehousing model that could enhance their sales. Previously, they had a reporting application that they were using on a semi-annual basis. Though they wanted to increase the frequency of their reporting and enable multiple users to run reports simultaneously, they didn’t have the internal expertise to confidently navigate these challenges.

Working with 2nd Watch made a clear difference. Our client was able to leverage a data warehouse architecture that provided daily data availability (in addition to the six-month snapshot) and self-service dashboards that didn’t require changes or updates on their part. We also set them on the right path to leverage a single source of the truth through future developments.

Our strategies in that project prioritized our client’s people instead of a specific technology. We considered the reporting and analytics needs of their business users rather than pigeonholing their business into a specific tool. Through our tech-agnostic approach, we guided them toward a future state that provided strategic advantage and a clear ROI that might have otherwise gone unachieved.

Want your data warehouse to provide you with a single source of the truth? Schedule a whiteboard session to review your options and consolidate your data into actionable insight.

Benefits of a Data Vault Model for 3PLs and How It Can Help Drive Better Decisions

As many third-party logistics (3PL) companies transition to a data-driven approach, it’s essential to underscore the importance of your data management practices. The way you choose to organize and store data impacts everything from how fast you can access information to which metrics are available. Many data-forward 3PL companies have begun implementing a data vault model to address this strategic decision. The data vault model allows them to address industry-wide challenges such as disparate data, lack of visibility into what is happening, reworking of analytics when acquisitions occur, and slow retrieval or transfer of information.

To assist you in determining the best possible way to organize your data, we will outline the benefits of a data vault model for 3PLs and highlight four use cases to illustrate the benefits for better decision-making.

What is data vault?

A data vault model is known for its practice of separating your data’s primary keys, relationships, and attributes from each other. Let’s say you want to analyze which customers are moving the most loads through you. The relationship between the customer and the load would be stored in one table, while the details about each load and customer would be stored in two separate, but related tables.

Structuring data in this manner helps you account for changing relationships within your data and seamless integration of new data sources when acquisitions or business rules inevitably change. Additionally, it enables quicker data loading through parallel streams and automatically stores historical data. For more details on what a data vault model is and the benefits it provides, check out this blog by 2nd Watch.

Data vault makes it easier to build a data warehouse with accurate, centralized data

The built-in relationships between data vault entities (hubs, satellites, links) make it easier to build a data warehouse. Structuring your data model around flexible but integrated primary keys allows you to combine data from various source systems easily in your data warehouse. It helps you ensure the data loaded into your reporting is not duplicated or out of date.

A lack of a data governance strategy often means that reporting is inconsistent and inaccurate. It reduces executives’ visibility into departments throughout the organization and limits your ability to create effective reporting because data is disjointed. Implementing a data vault model inherently accounts for centralizing your source data and enforcing primary keys. This will not only allow you to offer better reporting to customers, but it has also been found that accurate data is key to shipping accuracy. A strong data warehouse will further your internal analytics abilities by unlocking dashboards that highlight key metrics from revenue to cost-per-pound or on-time performance.

Data vault models make it easy to add new data sources and update business rules without interrupting access to data

A data vault model enables you to centralize data from various sources, while still addressing their differences such as load frequency and metadata. This is accomplished by storing the primary keys for an entity in one table, then creating attribute tables (satellites) specific to separate source systems.

Under a traditional model, most of this data would be held in one table and would require changes to the table structures, and therefore interruptions to data in production, each time a new source system is added. A scalable data model, like data vault, allows you to quickly adjust data delivery and reporting if your customers expand to new markets or merge with another company. Not only will this satisfy your current customers, but it is additionally a quality many logistics companies seek when choosing a 3PL partner. Accommodating multiple source systems and implementing business rules flexibly is key for any 3PL company’s data solution.

Data vault models allow for parallel loading, which gets you and your customers access to data faster

Data vault separates its source systems and data components into different tables. In doing so, it eliminates dependencies within your data and allows for parallel loading, meaning that multiple tables can be loaded at once rather than in a sequence. Parallel loading dramatically reduces the time it takes to access refreshed data.

Many 3PL companies offer customers access to high-quality reporting. Implementing a data vault model to load data quicker allows customers to gain insights in near-real-time. Furthermore, key metrics such as order accuracy, return rates, and on-time shipping percentage rely on timely data. They either require you to respond to a problem or could become inaccurate if your data takes too long to load. The faster you access your data, the more time you have to address your insights. This ultimately enables you to increase your accuracy and on-time shipments, leading to more satisfied customers.

Data vault models automatically save historic data required for advanced analytics

Whether you are looking for more advanced forecasting or planning to implement machine learning analytics, you will need to rely on historical data. Satellite tables, mentioned previously, store attribute information. Each time a feature of an order, a shipment, an employee, etc., changes, it is recorded in a satellite table with a timestamp when the change occurred. The model tracks the same information for changing relationships. This data allows you to automatically tie larger events to specific attributes involved when the events occurred.

3PL companies without data vault models often lose this history of attributes and relationships. When they pursue initiatives to find nuanced trends within their data through advanced analytics, their implementation is roadblocked by the task of generating adequate data. Alternatively, 3PL companies with a data vault model are ready to hit the ground running. Having historical data at your fingertips makes you prepared for any advanced analytics strategy.

2nd Watch has vast experience integrating 3PL companies’ key financial and operational data into a centralized hub. This immediately enables quick, reliable, and holistic insights to internal stakeholders and customers. Furthermore, it lays the groundwork for advanced predictive analytics that allow your teams to proactively address key industry challenges, including late deliveries, volatile market rates, and equipment failure.

Reach out to 2nd Watch for assistance getting started with data vault or evaluating how it may fit in with your current data strategy.

3 Options for Getting Started with a Modern Data Warehouse

In previous blog posts, we laid out the benefits of a modern data warehouse, explored the different types of modern data warehouses available, and discussed where a modern data warehouse fits in your overall data architecture.

Download Now: Modern Data Warehouse Comparison Guide [Snowflake, Amazon Redshift, Azure Synapse, and Google BigQuery]

There is no such thing as a one-size-fits-all data warehouse. To that end, there is no singular approach to getting started. Getting started depends on your goals, needs, and where you are today. In this blog post, we’ll outline a few options 2nd Watch offers for getting started with a modern data warehouse and the details for each.

Option 1: Data Architecture Whiteboard Session
Option 2: Modern Data Warehouse Strategy Session
Option 3: Modern Data Warehouse Quickstart

Option 1: 60-Minute Data Architecture Assessment

A 60-minute data architecture assessment is a great option to see how a modern data warehouse would fit in your current environment and what would be involved to get from where you are now to where you want to be.

During this session, we will outline a plan to achieve your goals and help you understand the tools, technologies, timeline, and cost to get there.

Who is this for? Organizations in the very early planning stages

Duration: 60 minutes

More Information

Option 2: Modern Data Warehouse Strategy

In order to see ROI and business value from your modern data warehouse, you must have a clear plan on how you are going to use it. During a modern data warehouse strategy project, our team will work with your stakeholders to understand your business goals and design a tech strategy to ensure business value and ROI from your data environment.

Who is this for? Organizations in the early planning stages looking to establish the business use case, cost benefits, and ROI of a modern data warehouse before getting started

Duration: 2-, 4-, 6-, or 8-week strategies are available

More Information

Option 3: Modern Data Platform Quickstart

You have your strategy laid out and are ready to get started ASAP. The modern data platform quickstart is a great option to get your modern data warehouse up and running in as few as six weeks.

During this quickstart, we’ll create a scalable data warehouse; clean, normalize, and ingest data; and even provide reports for predefined use cases.

Who is this for? Organizations that have outlined their strategy and are ready to start seeing the benefits of a modern data warehouse

Duration: 6 weeks

More Information

Not sure where to begin? We recommend beginning with a 60-minute data architecture assessment. This session allows us to walk through your current architecture, understand your organization’s pain points and goals for analytics, brainstorm on a future state architecture based on your goals, and then come up with next steps. Furthermore, the assessment allows us to determine if your organization needs to make a change, what those changes are, and how you might go about implementing them. Simply put, we want to understand the current state, learn about the future state of what you want to build toward, and help you create a plan so you can successfully execute on a modern data warehouse project.

A Word of Warning

Modern data warehouses are a big step forward from traditional on-premise architectures. They allow organizations to innovate quicker and provide value to the business much faster. An organization has many options in the cloud and many vendors offer a cloud data warehouse, but be careful: building a modern data warehouse architecture is highly involved and may require multiple technologies to get you to the finish line.

The most important thing to do when embarking on a modern data warehouse initiative is to have an experienced partner to guide you through the process the right way from establishing why a cloud data warehouse is important to your organization to outlining what the future state vision should be to develop a plan to get you there.

Data warehouse architecture is changing, don’t fall behind your competition! With multiple options for getting started, there is no reason to wait.

We hope you found this information valuable. If you have any questions or would like to learn more, please contact us and we’ll schedule a time to connect.

4 Issues in Data Migration from Legacy Systems to Avoid

The scales have finally tipped! According to a Flexera survey, 93% of organizations have a multi-cloud strategy and 53% are now operating with advanced cloud maturity. For those who are now behind the bell curve, it’s a reminder that keeping your data architecture in an on-premises solution is detrimental to remaining competitive. On-prem architecture restricts your performance and the overall growth and complexity of your analytics. Here are some of the setbacks of remaining on-prem and the benefits of data migration from legacy systems.

Looking for the right path to data modernization? Learn about our 60-minute data architecture assessment and how it will get you there.

Greater Decentralization

For most organizations, data architecture did not grow out of an intentional process. Many on-prem storage systems developed from a variety of events ranging from M&A activity and business expansion to vertical-specific database initiatives and rogue implementations. As a result, they’re often riddled with data silos that prevent comprehensive analysis from a single source of truth.

When organizations conduct reporting or analysis with these limitations, they are at best only able to find out what happened – not predict what will happen or narrow down what they should do. The predictive analytics and prescriptive analytics that organizations with high analytical maturity are able to conduct are only possible if there’s a consolidated and comprehensive data architecture.

Though you can create a single source of data with an on-prem setup, a cloud-based data storage platform is more likely to prevent future silos. When authorized users can access all of the data from a centralized cloud hub, either through a specific access layer or the whole repository, they are less likely to create offshoot data implementations.

Slower Query Performance

The insights from analytics are only useful if they are timely. Some reports are evergreen, so a few hours, days, or even a week doesn’t alter the actionability of the insight all that much. On the other hand, real-time analytics or streaming analytics requires the ability to process high-volume data at low latency, a difficult feat for on-prem data architecture to achieve without enterprise-level funding. Even mid-sized businesses are unable to justify the expense – even though they need the insight available through streaming analysis to keep from falling behind larger industry competitors.

Using cloud-based data architecture enables organizations to access much faster querying. The scalability of these resources allows organizations of all sizes to ask questions and receive answers at a faster rate, regardless of whether it’s real-time or a little less urgent.

Plus, those organizations that end up working with a data migration services partner can even take advantage of solution accelerators developed through proven methods and experience. Experienced partners are better at avoiding unnecessary pipeline or dashboard inefficiencies since they’ve developed effective frameworks for implementing these types of solutions.

More Expensive Server Costs

On-prem data architecture is far more expensive than cloud-based data solutions of equal capacity. When you opt for on-prem, you always need to prepare and pay for the maximum capacity. Even if the majority of your users are conducting nothing more complicated than sales or expense reporting, your organization still needs the storage and computational power to handle data science opportunities as they arise.

All of that unused server capacity is expensive to implement and maintain when the full payoff isn’t continually realized. Also, on-prem data architecture requires ongoing updates, maintenance, and integration to ensure that analytics programs will function to the fullest when they are initiated.

Cloud-based data architecture is far more scalable, and providers only charge you for the capacity you use during a given cycle. Plus, it’s their responsibility to optimize the performance of your data pipeline and data storage architecture – letting you reap the full benefits without all of the domain expertise and effort.

Hindered Business Continuity

There’s a renewed focus on business continuity. The recent pandemic has illuminated the actual level of continuity preparedness worldwide. Of the organizations that were ready to respond to equipment failure or damage to their physical buildings, few were ready to have their entire workforce telecommuting. Those with their data architecture already situated in the cloud fared much better and more seamlessly transitioned to conducting analytics remotely.

The aforementioned accessibility of cloud-based solutions gives organizations a greater advantage over traditional on-prem data architecture. There is limited latency when organizations need to adapt to property damage, natural disasters, pandemic outbreaks, or other watershed events. Plus, the centralized nature of this type of data analytics architecture prevents unplanned losses that might occur if data is stored in disparate systems on-site. Resiliency is at the heart of cloud-based analytics.

It’s time to embrace data migration from legacy systems in your business. 2nd Watch can help! We’re experienced with migration legacy implementations to Azure Data Factory and other cloud-based solutions.

Let’s Start Your Data Migration

HVR, Riversand, ALTR, and Sisu | Snowflake-Adjacent Technologies You Need to Know About

Snowflake is a powerhouse in the data world, but it can become even more powerful when paired with other technologies that suit your organization’s needs. 2nd Watch is a tech-agnostic firm, meaning we only recommend tools we truly believe are the best for your business. Because Snowflake partners with such a wide range of technologies, we have lots of options to create a tech stack uniquely suited to you.

Among the many tools that work in conjunction with Snowflake, the following Snowflake-adjacent technologies enhance Snowflake in different ways and are all noteworthy in their own right.

HVR (a Fivetran company)

HVR, which became part of Fivetran as of late 2021, is a cloud data replication tool that natively supports Snowflake. It executes real-time data replication by reading directly from database transaction logs, which allows for high performance and low impact on database servers.

As one of the earliest technologies to join the Snowflake Partner Network, HVR understands how analytics efforts can be impacted by real-time data streaming to Snowflake. Using HVR to offer immediate access to your data in Snowflake’s Data Cloud, you can make timely decisions based on current, accurate data.

Riversand

Riversand is a master data management (MDM) tool that “goldenizes” your data. In other words, it creates one current, complete, and accurate record.

After connecting your organization with the right MDM tool for your situation, like Riversand, 2nd Watch would oversee the MDM implementation and provide guidance on next steps. This includes sharing insights on best-in-class data warehouses, like Snowflake’s Data Cloud. Together, Riversand and Snowflake can prepare you for downstream analytics and long-term data governance.

ALTR

ALTR is a cloud-native Data Security as a Service (DSaaS) platform that helps companies optimize data consumption governance. With ALTR, you’re able to track data consumption patterns and limit how much data can be consumed at various levels across your organization.

ALTR and Snowflake work together in two ways:

ALTR seamlessly integrates with Snowflake so you can view your securely shared data consumption information.
With data security at the SQL layer, ALTR improves upon Snowflake’s built-in security to stop and prevent threats within Snowflake.

Sisu

Sisu accelerates and expands your analytics capabilities. Using Sisu, business users are able to self-serve and find the answers to increasingly complex questions.

Sisu connects directly to Snowflake’s Data Cloud, so you always have access to your most up-to-date data for comprehensive, actionable analytics. Sisu and Snowflake partner to empower you to make quick, decisive judgments.

These four technologies are just a taste of the many tools that work with Snowflake to improve your organization’s data and analytics capabilities. To discuss your specific data and analytics needs – and to determine the tools Snowflake partners with that will help you meet those needs – contact 2nd Watch today.

The Data Supply Chain for Marketers

In this blog post, we’ll explore the basics of the data supply chain and why they matter for marketers, including:

Data ingestion
The differences between ETL and ELT
Data pipelines
Data storage options

If you haven’t read Data 101 for Marketers, start with that blog post here.

Data Ingestion

Data ingestion is the first step in any analytical undertaking. It’s a process where data from one or many sources are gathered and imported into one place. Data can be imported in real time (like POS data) or in batches (like billing systems).

Why It Matters for Marketers:

The process of data ingestion consolidates all of the relevant information from across your data sources into a single, centralized storage system. Through this process, you can begin to convert disparate data created in your CRM, POS, and other source systems into a unified format that is ready for real-time or batch analysis.

Real-World Examples:

Marketing teams pull data from a wide variety of resources, including Salesforce, Marketo, Facebook, Twitter, Google, Stripe, Zendesk, Shopify, Mailchimp, mobile devices, and more. It’s incredibly time-consuming to manually combine these data sources, but by using tools to automate some of these processes you can get data into the hands of your team faster.

This empowers marketers to answer more sophisticated questions about customer behavior, such as:

Why are customers leaving a specific product in their online shopping carts?
What is the probability that we’ll lose a customer early in the customer journey?
Which messaging pillar is resonating most with customers in the middle of the sales funnel who live in Germany?

Image 1: In this image, three source systems with varying formats and content are ingested into a central location in the data warehouse.

ETL vs. ELT

ETL and ELT are both data integration methods that make it possible to take data from various sources and move it into a singular storage space like a data warehouse. The difference is in when the transformation of data takes place.

Real-World Examples:

As your business scales, ELT tools are better-equipped to handle the volume and variety of marketing data on hand. However, a robust data plan will make use of both ELT and ETL tools.

For example, a member of your team wants to know which marketing channels are the most effective at converting customers with the highest average order value. The data you need to answer that question is likely spread across multiple structured data sources (e.g., referral traffic from Google Analytics, transaction history from your POS or e-commerce system, and customer data from your CRM).

Through your ETL process, you can extract relevant data from the above sources, transform it (e.g., updating customer contact info across files for uniformity and accuracy), and load the clean data into one final location. This enables your team to run your query in a streamlined way with limited upfront effort.

In comparison, your social media marketing team wants to see whether email click-through rates or social media interactions lead to more purchases. The ELT process allows them to extract and load all of the raw data in real time from the relevant source systems and run ad-hoc analytics reports, making adjustments to campaigns on the fly.

Extract, Transform, Load (ETL)

This method of data movement first copies data from the original database into the target system and then converts the data into a singular format. Lastly, the transformed data is uploaded into a data warehouse for analytics.

When You Should Use ETL:

ETL processes are preferable for moving small amounts of structured data with no rush on when that data is available for use. A robust ETL process would clean and integrate carefully selected data sources to provide a single source of truth that delivers faster analytics and makes understanding and using the data extremely simple.

Image 2: This image shows four different data sources with varying data formats being extracted from their sources, transformed to all be formatted the same, and then loaded into a data warehouse. Having all the data sources formatted the same way allows you to have consistent and accurate data in the chart that is built from the data in the data warehouse.

Extract, Load, Transform (ELT)

Raw data is read from source databases, then loaded into the database in its raw form. Raw data is usually stored in a cloud-based data lake or data warehouse, allowing you to transform only the data you need.

When You Should Use ELT:

ELT processes shine when there are large amounts of complex structured and unstructured data that need to be made available more immediately. ELT processes also upload and store all of your data in its raw format, making data ingestion faster. However, performing analytics on that raw data is a more complex process because cleaning and transformation happen post-upload.

Image 3: This image is showing four different data sources with the data formatted in different ways. The data is being extracted from the various sources, loaded into the data warehouse, and then transformed within the data warehouse to all be formatted the same. This allows for accurate reporting of the data in the chart seen above.

Data Pipeline

A data pipeline is a series of steps in an automated process that moves data from one system to another, typically using ETL or ELT practices.

Why It Matters for Marketers:

The automatic nature of a data pipeline removes the burden of data manipulation from your marketing team. There’s no need to chase down the IT team or manually download files from your marketing automation tool, CRM, or other data sources to answer a single question. Instead, you can focus on asking the questions and honing in on strategy while the technology takes away the burden of tracking down, manipulating, and refreshing the information.

Real-World Examples:

Say under the current infrastructure, your sales data is split between your e-commerce platform and your in-store POS systems. The different data formats are an obstacle to proper analysis, so you decide to move them to a new target system (such as a data warehouse).

A data pipeline would automate the process of selecting data sources, prioritizing the datasets that are most important, and transforming the data without any micromanagement of the tool. When you’re ready for analysis, the data will already be available in one destination and validated for accuracy and uniformity, enabling you to start your analysis without delay.

Data Storage Options

Databases, data warehouses, and data lakes are all systems for storing and using data, but there are differences to consider when choosing a solution for your marketing data.

Definitions:

A database is a central place where a structured and organized collection of data can be stored in a computer that is accessed via various applications such as MailChimp, Rollworks, Marketo, or even more traditional campaigns like direct mail. It is not meant for large-scale analytics.
A data warehouse is a specific way of structuring your data in database tables so that it is optimized for analytics. A data warehouse brings together all your various data sources under one roof and structures it for analytics.
A data lake is a vast repository of structured and unstructured data. It handles all types of data, and there is no hierarchy or organization to the storage.

Why It Matters for Marketers:

There are benefits and drawbacks to each type of data structure, and marketers should have a say in how data gets managed throughout the organization. For example, with a data lake, you will need to have a data scientist or other technical resource on staff to help make sense of all the data, but your marketing team can be more self-sufficient with a database or data warehouse.

Database

Benefits:

Without organization and structure, the insights your data holds can be unreliable and hard to find. Pulling data from various source systems is often time-consuming and requires tedious and error-prone reformatting of the data in order to tell a story or answer a question. A database can help to store data from multiple sources in an organized central location.

Real-World Example:

Without databases, your team would have to use multiple Excel sheets and manual manipulation to store the data needed for analysis. This means your team would have to manually match up or copy/paste each Excel sheet’s data in order to create one place to analyze all of your data.

Data Warehouse

Benefits:

A data warehouse delivers an extra layer of organization across all databases throughout your business. Your CRM, sales platform, and social media data differ in format and complexity but often contain data about similar subjects. A data warehouse brings together all of those varying formats into a standardized and holistic view structured to optimize reporting. When that data is consolidated from across your organization, you can obtain a complete view of your customers, their spending habits, and their motivations.

Real-World Example:

You might hear people say “enterprise data warehouse” or “EDW” when they talk about data. This is a way to structure data that makes answering questions via reports quick and easy. More importantly, EDWs often contain information from the entire company, not just your function or department. Not only can you answer questions about your customer or marketing-specific topics, but you can understand other concepts such as the inventory flow of your products. With that knowledge, you can determine, for example, how inventory delays are correlated to longer shipping times, which often result in customer churn.

Data Lake

Benefits:

A data lake is a great option for organizations that need more flexibility with their data. The ability for a data lake to hold all data—structured, semi-structured, or unstructured—makes it a good choice when you want the agility to configure and refigure models and queries as needed. Access to all the raw data also makes it easier for data scientists to manipulate the data.

Real-World Example:

You want to get real-time reports from each step of your SMS marketing campaign. Using a data lake enables you to perform real-time analytics on the number of messages sent, the number of messages opened, how many people replied, and more. Additionally, you can save the content of the messages for later analysis, delivering a more robust view of your customer and enabling you to increase personalization of future campaigns.

So, how do you choose?

You might not have to pick just one solution. In fact, it might make sense to use a combination of these systems. Remember, the most important thing is that you’re thinking about your marketing data, how you want to use it, what makes sense for your business, and the best way to achieve your results.

Hopefully this information has helped you better understand your options for data ingestion and storage. Feel free to contact us with any questions or to learn more about data ingestion and storage options for your marketing data.

Get to Know ALTR: Optimizing Data Consumption Governance

ALTR is a cloud native DSaaS platform designed to optimize data consumption governance. In this age of ever-expanding data security challenges, which have only increased with the mass move to remote workforces, data-centric organizations need to easily but securely access data. Enter ALTR: a cloud-native platform delivering Data Security as a Service (DSaaS) and helping companies to optimize data consumption governance.

Not sure you need another tool in your toolkit? We’ll dive into ALTR’s benefits so you can see for yourself how this platform can help you get ahead of the next changes in data security, simplify processes and enterprise collaboration, and maximize your technology capabilities, all while staying in control of your budget.

How Does ALTR Work?

With ALTR, you’re able to track data consumption patterns and limit how much data can be consumed. Even better, it’s simple to implement, immediately adds value, and is easily scalable. You’ll be able to see data consumption patterns from day one and optimize your analytics while keeping your data secure.

ALTR delivers security across three key stages:

Observe – ALTR’s DSaaS platform offers critical visibility into your organization’s data consumption, including an audit record for each request for data. Observability is especially critical as you determine new levels of operational risk in today’s largely remote world.

Detect and Respond – You can use ALTR’s observability to understand typical data consumption for your organization and then determine areas of risk. With that baseline, you’re able to create highly specific data consumption policies. ALTR’s cloud-based policy engine then analyzes data requests to prevent security incidents in real time.

Protect – ALTR can tokenize data at its inception to secure data throughout its lifecycle. This ensures adherence to your governance policies. Plus, ALTR’s data consumption reporting can minimize existing compliance scope by assuring auditors that your policies are solid.

What Other Benefits Does ALTR Offer?

ALTR offers various integrations to enhance your data consumption governance:

Share data consumption records and security events with your favorite security information and event management (SIEM) software.

View securely shared data consumption information in Snowflake.

Analyze data consumption patterns in Domo.

ALTR delivers undeniable value through seamless integration with technologies like these, which you may already have in place; paired with the right consultant, the ROI is even more immediate. ALTR may be new to you, but an expert data analytics consulting firm like 2nd Watch is always investigating new technologies and can ease the implementation process. (And if you need more convincing, ALTR was selected as a finalist for Bank Director’s 2020 Best of FinXTech Awards.)

Dedicated consultants can more quickly integrate ALTR into your organization while your staff stays on top of daily operations. Consultants can then put the power in the hands of your business users to run their own reports, analyze data, and make data-driven decisions. Secure in the knowledge your data is protected, you can encourage innovation by granting more access to data when needed.

As a tech-agnostic company, 2nd Watch helps you find the right tools for your specific needs. Our consultants have a vast range of product expertise to make the most of the technology investments you’ve already made, to implement new solutions to improve your team’s function, and to ultimately help you compete with the companies of tomorrow. Reach out to us directly to find out if ALTR, or another DSaaS platform, could be right for your organization.