Digital Transformation

The amount of data produced today is mind-boggling and is expanding at an astonishing rate. This explosion of data has led to the emergence of big data challenges that organizations must overcome to remain competitive. Organizations must effectively manage data, guarantee its accuracy and quality, in order to derive actionable insights from it. This is where DataOps comes in. In this first post we go through an overview of DataOps and in our next post we will discuss tooling that support DataOps as well as discuss companies that have seen the benefit of implementing DataOps Processes.

What is DataOps?

DataOps is a methodology that merges DevOps principles from software development with data management practices. This helps to improve the process of developing, deploying, and maintaining data-driven applications. The goal is to increase the delivery of new insights and the approach emphasizes collaboration between data scientists, data engineers, data analysts, and analytics engineers.

Automation and continuous delivery are leveraged for faster deployment cycles. Organizations can enhance their data management procedures, decrease errors, and increase the accuracy and timeliness of their data by implementing DataOps.

The overarching goal is to improve an organization's capacity for decision-making by facilitating quicker, more precise data product deliveries. DataOps can speed up task completion with fewer errors by encouraging effective team collaboration, ultimately enhancing the organization's success.

DataOps is now feasible thanks to modern data stack tools. Modern data tools like Snowflake and dbt can enable delivery of new features more quickly while upholding IT governance standards. This allows for employee empowerment and improves their agility all while helping to create a culture that values data-driven decision-making. Effective DataOps implementation enables businesses to maximize the value of their data and build a data-driven culture for greater success.

The importance of DataOps in solving big data challenges

It is impossible to overstate the role that DataOps plays in overcoming big data challenges. Organizations must learn how to manage data effectively as it continues to increase if they want to stay competitive.

DataOps addresses these challenges by providing a set of best practices and tools that enable data teams to work more efficiently and effectively. Some of the key benefits of DataOps include:

1. Improved agility and flexibility: DataOps empowers data teams to work in an agile manner, enabling them to quickly adapt to shifting requirements and business needs. By basing decisions on up-to-the-minute information, businesses can stay one step ahead of the competition.
2. Increased collaboration: DataOps encourages collaboration between different teams involved in the data management process, including data engineers, data scientists, and business analysts. This leads to better communication, faster feedback loops, and more efficient workflows.
3. Enhanced quality and accuracy: DataOps lowers the risk of errors and inconsistencies in the data by automating data deliverable processing and analysis pipelines. As a result, businesses are better able to make decisions based on accurate and trustworthy data
4. Reduced costs: DataOps helps businesses save money by reducing the time and resources needed to manage and perform data analytics. As a result, they can invest in other aspects of their business to maintain their competitiveness.

In a world without DataOps, data-related processes can be slow, error-prone, and documentation is often outdated. DataOps provides the opportunity to rethink how data products are delivered. This promotes a culture of federated delivery. Everyone in the organization with the necessary skills is empowered to participate in insight generation.

We can achieve comparable results while preserving governance over the delivery process by learning from large, distributed open-source software development projects like the Linux project. To ensure high-quality output, a good DataOps process places an emphasis on rules and procedures that are monitored and controlled by a core team.

What are the components for successful DataOps implementation?

To implement DataOps well, one needs to take a holistic approach that brings together people, processes, and technology. Here are the five most important parts of DataOps that organizations need to think about in order to implement it successfully:

1. Collaborative Culture: Organizations must foster a collaborative culture and they must encourage cross-functional teamwork and make it easy for data scientists, data engineers, data analysts, and analytics engineers to talk to each other and have visibility into each other's work. Collaboration breaks down silos and makes it easier for everyone to understand how data-driven applications work. A collaborative culture also helps team members feel like they own their work and are responsible for it, which can help improve the quality of the data and the applications that use it.
2. Automation: Automation is a key part of DataOps because it helps organizations streamline their feature deliveries and cut down on mistakes. Data ingestion, data integration, and data validation are all examples of tasks that are done over and over again. By automating these tasks, organizations can free up their resources to focus on more complex and valuable tasks, like analyzing data and deriving insights from it.
3. Continuous Integration and Delivery: Continuous integration and delivery (CI/CD) are important parts of DataOps. Together they enable the deployment of working code to production in a continuous and iterative way. CI/CD can help speed up the time it takes to get data-driven apps to market so companies can respond quickly to changing business needs and provide more value to their customers.
4. Monitoring and Feedback: Monitoring and feedback are important parts of DataOps because they help make sure that data-driven applications work as they should. By keeping an eye on how well data-driven applications work, organizations can catch problems early and fix them as needed. By asking users for feedback, the code can be made more reliable and maintainable leading to better better outcomes.
5. Data Governance: Data governance is a set of policies, procedures, and controls that make sure data is managed correctly throughout its lifecycle. Data governance includes data ownership, data quality, data security, data privacy, and data lineage. By putting in place strong data governance practices, organizations can make sure that their data is correct, consistent, and in line with company standards. Data governance can help make the data more trustworthy and lower the chance of a data breach.

Having identified what needs to be done to achieve a good DataOps implementation, you may be wondering how we turn these ideas into actionable steps.

What are the best ways to implement DataOps in organizations?

1. Start Small and Grow: When putting DataOps in place, it's important to start small and grow gradually. This method lets companies try out and improve their DataOps processes before putting them to use on bigger projects. By starting out small, organisations can find potential problems and solve them before rolling them out to a larger audience.
2. Align DataOps with Business Goals: For an organization's DataOps implementation to work, its DataOps processes need to be in line with its business goals. Organizations should figure out what their most important business goals are for their data-driven applications, and then design their DataOps processes to help them reach these goals. This approach makes sure that DataOps is focused on adding value to the business and not just on implementing new technology.
3. Foster collaboration between teams: One of the most important parts of DataOps is getting teams to work together. Organizations need to help data scientists, data engineers, data analysts, and analytics engineers work together. This method breaks down silos and makes it easier for everyone to understand how data-driven applications work. Organizations can make it easier for people to work together by putting them on cross-functional teams, encouraging open communication, fostering transparency, and where possible encouraging the same tools, e.g. using dbt for data pipelines.
4. Invest in tools for automation. Automation is a key part of DataOps. Organizations should invest in automation tools that can help them speed up their data management processes, such as data ingestion, data integration, and data validation. Automation can help cut down on mistakes, increase productivity, and free up resources to be used on more complicated tasks, like analyzing data.
5. Implement CI/CD processes: Creating a robust feature release process with automated validations and code review will encourage collaboration and build transparency in the process. Users will be encouraged to move faster by removing the fear that a new feature will break production.
6. Monitor Performance and Get Feedback: Two important parts of DataOps are monitoring performance and getting feedback. Organizations should use monitoring tools that can keep track of how well data-driven applications are working and get feedback from users. This method helps find problems early on and makes the data more accurate and reliable. It also gives organizations useful information about how users leverage tools and this can help them improve the user experience.

Looking to implement DataOps quickly and efficiently?  

At Datacoves, we offer a comprehensive suite of DataOps tools to help organizations implement robust processes quickly and efficiently. One of the key benefits of using Datacoves is the ability to implement DataOps incrementally using our dynamic Infrastructure. Organizations can start by automating simple processes, such as data collection and cleaning, and gradually build out more complex processes as their needs evolve.

Our agile methodology allows for iterative development and continuous deployment, ensuring that our tools and processes are tailored to meet the evolving needs of our clients. This approach allows organizations to see the benefits of DataOps quickly, without committing to a full-scale implementation upfront. We have helped teams implement mature DataOps processes in as little as six weeks.

The Datacoves platform is being used by top global organizations and our team has extensive experience in guiding organizations through the DataOps implementation process. We ensure that you benefit from best practices and avoid common pitfalls when implementing DataOps.

Set aside some time to speak with us to discuss how we can support you in maturing your data processes with dbt and DataOps.

Don't miss out on our latest blog, where we showcase the best tools and provide real-life examples of successful implementations.

Get ready to take your data operations to the next level!

Big Data Analytics - Making information tasty and accessible

No one bakes for the sake of baking alone; cakes are meant to be shared.  If no one bought, ate, or gifted someone with their delicious chocolate cake, then there would be no bakers. The same is true for data analytics. Our goal as data practitioners is to feed our organization with the information needed to make decisions.  

If our cake doesn’t taste good or isn’t available when people want dessert, then it doesn’t matter that we made it from scratch. When it comes to big data, your goal should be to have the equivalent of a delicious cake - usable data - available when someone needs it.

Big Data Analytics - The ever-changing tastes of data consumers

Life would be simple if everyone were happy with a single flavor of cake. Metrics play a crucial role in our organization, and two of the most fundamental ones are ARR (Annual Recurring Revenue) and NRR (Net Recurring Revenue). These metrics are like chocolate and vanilla - they remain popular and relevant. Yet, these flavors alone are not enough. Just as with ice cream flavors, we eventually need to try something new, the same goes for insights. It's important to experiment and explore different perspectives.

When something is novel, we love it. When we first start baking, we are not consistent. But, over time, quality improves. We go from ok, to good, and to great. Even with our newfound expertise, the chocolate cake will become boring. We all want something new.  

With data, you often start with a simple metric. Having something is better than nothing, but that only lasts so long. We discover something new about our business and we translate that information into action. This is good for a while, but we will eventually see diminishing returns. While a LTV (Life Time Value) analysis may have a significant impact today, its usefulness is likely to diminish within a short period of time. Your stakeholders will crave something new, something more innovative and updated.

Someone is going to ask for a deep dive into how CAC (Customer Acquisition Cost) impacts LTV, or they might ask for a new kind of icing on that cake you just made. Either way, the point remains – as those around you start making use (or eating) of what you’re providing, they will inevitably ask for something more.  

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Big Data Analytics - How much expertise do you need to bake successfully?

It depends. Some organizations can do more than enough with the reports and dashboards built into their CRM, web analytics, or e-commerce system. This works for many companies. There is no need to spend extra time and money on more complex data systems when something simple will suffice. There is a reason that chocolate and vanilla are popular flavors; many people like their taste and know what they are getting with their order. The same can be said for your data infrastructure.

However, flexibility is the challenge. You are limited by your reporting options and the data analytics you can access. You can have a cake you can eat, but it’s going to look a certain way, having only one type of icing, and it certainly will not have any premium fillings.  

If you want those things, you need to look for something a bit more nuanced.  

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Big Data Analytics - Start with an Easy Bake Oven

How do you start to accommodate your organization’s new demand for analysis or your family’s newly refined palate? By using new tools and techniques.  

The Easy Bake Oven is the simplest first step – you get a pouch of ingredients, mix them, and within a few minutes, you have a cake. You might think it’s a children’s toy, but there are very creative recipes out there for the adventurous types. Unfortunately, you can only go so far; you’re limited by the size of the oven and the speed you can bake each item.  

The corollary in the data world is Excel – a fabulous tool, but something that has its limitations. While it is possible to extract data from your tools and manipulate it in a spreadsheet to make it more manageable, you are still limited by the pre-designed extracts offered by the vendor tools. Excel is flexible enough for many, but it is not a perfect end-state reporting solution for everyone.  

Eventually, you’ll need to address inconsistencies between systems and automate the process of data prep before it gets into your spreadsheet.

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Big Data Analytics - Using more advanced techniques to elevating your baking

We have a feel for the basics, but now we want to improve our process. There are certain things we do repeatedly, regardless of the recipes we’re making. We need to create an assembly line. Whether we’re calculating metrics or mixing the wet ingredients and the dry ingredients, there are steps we need to complete in a specific order and to a certain level of quality.  

We’ve moved on from the Easy Bake Oven and are now baking using a full-size oven. We need to be more careful about our measurements, ensure the oven is at the correct temperature, and write down notes about the various steps in our more complicated recipes. We need to make sure that different cakes, fillings, and decorations are ready at the same time, even if they have wildly different prep times. We need consistent results every time we make the cake, and we need others to be able to make the same recipe and achieve the same results by following our instructions.

Documenting and transferring this knowledge to others is difficult. Sure, you can write things down, but it is easy to skip a step that you take for granted. Perhaps your handwriting is hard to read, or someone is having trouble with the oven. You may be aware that using a hair dryer while baking can cause the circuit breaker to trip, but unless you document this information, others will not know this quirk. If your recipe has changed, such as using individual ingredients instead of a pre-made cake mix, it is important to clearly specify these adjustments, otherwise your friends and family may struggle to replicate your delicious cake.

There are plenty of data analytics tools out there that assist at this stage – Alteryx, Tableau Prep, and Datameer are just a few of many. In large enterprise organizations, you might find Informatica Power Center, Talend, or Matillion. These types of tools have graphical user interfaces (GUI); they give you the flexibility to extract data, load data, inspect data and transform it. Many enable you to define and calculate metrics. But they require you to work within each tool’s set of rules and constraints. This works well if you are starting and need something less complex.

The process that was once simple now is not; there are hidden assumptions, configurations, and requirements. You’re not using a pen-and-paper recipe anymore; now you’re working within a new system.  

GUI-based tools are great for companies whose workflows fit the way the systems work. But between the way some tools are licensed, and the skill needed to use them, they are only available to the IT organization. This leaves users to find shortcuts, develop workarounds, and become dependent on the business’ shadow IT. Inevitably, you’re going to run into maintainability issues.  

Recipes often have plenty of steps and ingredients; “data recipes” are no different. There are dependencies between operations, different run times for different transformations, different release cadences, and data availability SLAs. Your team might be able to manage your entire workflow quite well with one of these tools, but once your team starts introducing custom SQL logic or additional overlapping tools into the ecosystem, you introduce another layer of complexity. The result is an increase in the total cost of ownership.  

Often, this complexity is opaque, too. It is not obvious what that GUI component is doing, but they are strung together to build something usable. Over time, the complexity continues to grow; custom SQL logic is introduced, and more steps in the chain. Eventually, abstractions begin to form. The data engineers decide that these processing pipelines look quite similar and can be customized based on some basic configurations. Less overhead, more output.  

You’re now on the path of building custom ELT (Extract, Transform, and Load) pipelines, stitched together within the constraints of a GUI-based system. For some companies, this is okay, and it works. But there is a hidden cost – it is harder to maintain high quality inputs and outputs. The layers are tightly coupled and a mistake in one step is not caught until the whole pipeline is complete.  

IT may not be aware of downstream issues because they occur in other tools outside of their domain; one change here breaks something else there. This is like buying a ready-made cake mix and “enhancing it” with your custom ingredients. It works until it does not. One day, Duncan Hines changes their ingredients, and without you realizing, there is a bad reaction between your “enhancements” and the new mix.  Your once great recipe is not so great anymore, but there was no way for Duncan Hines to know. They expected you to follow their instructions; everything has been going according to plan until now.  

Even if your tool has strong version control built in, it’s often difficult to reverse the changes before it is too late. If your recipe calls for 1 cup of sugar but you accidentally add in 11, we don’t want to wait until the cake is baked to discover the error. We want to catch that mistake as soon as it happens.  

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Big Data Analytics - Building consistence into the baking process

Everything up to this point serves a specific profile of a business, but what happens when the business matures beyond what these tools offer? What happens when you know how to bake a cake, but struggle to consistently produce hundreds or thousands of the same quality cakes?

When we aim to expand our baking operations, it's crucial to maintain consistency among bakers, minimize accidental mistakes, and have the ability to swiftly recover from any errors that occur. We need enough mixers and ovens to support the demand for our cakes, and we need an organized pantry with the correct measuring cups and spoons. We need to know which ingredients are running low, which are delayed, and which have common allergens.  

In data analytics, we have our own supply chain, often called ELT. You will find tools like Airbyte and Fivetran are common choices for bringing in our data “ingredient delivery”. They manage data extraction and ingestion so you can skip the manual CSV downloads that once served you so well.

We want to ensure quality, have traceability, document our process, and successfully produce and deliver our cakes. To do this, we need a repeatable process, with clear sequential steps. In the world of baking, we use recipes to achieve this.  

All baking recipes have a series of steps, some of which are common across different recipes. For example, creaming eggs and sugar, combining the wet and dry ingredients, and whipping the icing are repeatable steps that, when performed in the correct order, result in a delicious dessert. Recipes also follow a standard format: preheat the oven to the specified temperature, list the ingredients in the order in which they are used, and provide the preparation steps last. The sequence is intentional and provides a clear understanding of what to expect during the baking process.

We can apply this model to our data infrastructure by using a tool called dbt (data build tool). Instead of repeating miscellaneous transformation steps in various places, we can centralize our transformation logic into reusable components. Then we can reference those components throughout our project. We can also identify which data is stale, review the chain of dependencies between transformations, and capture the documentation alongside that logic.

We no longer need a GUI-driven tool to review our data; instead, we use the process as defined in the code to inform our logic and documentation. Our new teammate can now confidently create her cake to the same standard as everyone else; she can be confident that she is avoiding common allergens, too.

Better yet, we have a history of changes to our process and “data recipe”. Version control and code reviews are an expectation, so we know when modifications to our ingredient list will cause a complete change in the final product. Our recipes are no longer scattered, but part of a structured system of reusable, composable steps.  

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Big Data Analytics - Are you ready to bake well, consistently?

Maturing the way we build data processes comes down to our readiness. When we started with our Easy Bake Oven, little could go wrong, but little could be tweaked. As we build a more robust system, we can take advantage of its increased flexibility, but we also need to maintain more pieces and ensure quality throughout a more complex process.

We need to know the difference between baking soda and baking powder. Which utensils are best, and which oven cooks most evenly? How do we best organize our new suite of recipes? How do you set up the kitchen and install the appliances? There are many decisions to make, and not every organization is ready to make them. All this can be daunting for even large organizations.

But you don’t have to do things all at once. Many organizations choose to make gradual improvements, transforming their big data process from disorganized to consistent.

You can subscribe to multiple SaaS services like Fivetran or Airbyte for data loading and use providers like dbt Cloud for dbt development. If your work grows increasingly complex, you can make use of another set of tools (such as Astronomer or Dagster) to orchestrate your end-to-end process.  You will still need to develop the end-to-end flow, so what you gained in flexibility you have lost in simplicity.

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Big Data Analytics - Becoming a master baker quickly

This is what we focus on at Datacoves. We aim to help organizations create mature processes, even when they have neither the time nor resources to figure everything out.

We give you “a fully stocked kitchen” - all the appliances, recipes, and best practices to make them work cohesively. You can take the guess work out of your data infrastructure, and instead, use a suite of tools designed to help your team perform timely and efficient analytics.

Whether your company is early in its data analytics journey or ready to take your processes to the next level, we are here to help. If your organization has strong info-sec or compliance requirements, we can also deploy within your private cloud. Datacoves is designed to get you “baking delicious cakes” as soon as possible.

Set aside some time to speak with me and learn how Datacoves has helped both small and large companies deploy mature analytics processes from the start. Also check out our case studies to see some of our customer's journeys.

Imagine yourself baking the next great cake at your organization. You can do it quickly with our help.

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In 3 Core Pillars to a Data-Driven Culture, I discussed the reasons why decision makers don’t trust analytics. I then outlined the alignment and change management aspect to any solution. Once you know what you want, how do you deliver it? The cloud revolution has brought in a new set of challenges for organizations which have nothing to do with delivering solutions. The main problem is that people are faced with a Cheesecake Factory menu and most people would be better served with Omakase.

For those who may not be aware, The Cheesecake Factory menu has 23 pages and over 250 items to choose from. There are obviously people who want the variety and there is certainly nothing wrong with that, but my best meals have been where I have left the decision to the chef.

Omakase, in a Japanese restaurant is a meal consisting of dishes selected by the chef, it literally means “I'll leave it up to you”

How does this relate to the analytics landscape? Well, there is a gold rush in the analytics space. There is a lot of investment and there are literally hundreds of tools to choose from. I have been following this development over the last five years and if anything, the introduction of tools has accelerated.

Most people are where I was back in 2016. While I have been doing work in this space for many years the cloud and big data space was all new to me. There was a lot I needed to learn and I was always questioning whether I was making the right decision. I know many people today who do POC after POC to see which tool will work the best, I know, I did the same thing.

Contrast this process with my experience learning a web development framework called Ruby on Rails. When I started learning Rails in 2009 I was focused on what I was trying to build, not the set of tools and libraries that are needed to create a modern web application. That’s because Rails is Omakase.

When you select Omakase in Rails you are trusting many people with years of experience and training to share that knowledge with you. Not only does this help you get going faster, but it also brings you into a community of like-minded people. So that when you run into problems, there are people ready to help. Below I present my opinionated view of a three-course meal data stack that can serve most people and the rationale behind it. This solution may not be perfect for everyone, but neither is Rails. 

Appetizer: Loading data

You are hungry to get going and start doing analysis, but we need to start off slowly. You want to get the data, but where do you start. Well, there are a few things to consider.

·     Where is the data coming from?

·     Is it structured into columns and rows or is it semi-structured(JSON)?

·     Is it coming in at high velocity?

·     How much data are you expecting?

What I find is that many people want to over engineer a solution or focus on optimizing for one dimension which is usually cost since that is simple to grasp. The problem is that if you focus only on cost, you are giving up something else, usually a better user experience. You don’t have a lot of time to evaluate solutions and build extract and load scripts, so let me make this simple. If you start with Snowflake as your database and Fivetran as your Extract and Load solution, you’ll be fine. Yes, there are reasons why not to choose those solutions, but you probably don’t need to worry about them, especially if you are starting out and you are not Apple.

Why Snowflake you ask? Well, I have used Redshift, MS SQLServer, Databricks, Hadoop, Teradata, and others, but when I started using Snowflake I felt like a weight was lifted. It “just worked.” Do you think you will need to mask some data at some point? They have dynamic data masking. Do you want to be able to scale compute or storage independently? They have separate compute and storage too. Do you like waiting for data vendors to extract data from their system and then having to import it on your side? Or do you need to collaborate with partners and send them data? Well,Snowflake has a way for companies to share data securely, gone are the days of moving data around, now you can securely grant access to groups within or outside your organization, simple, elegant. What about enriching your data with external data sources? Well, they have a data marketplace too and this is bound to grow. Security is well thought out too and you can tell they are focused on the user experience because they do things to improve analyst happiness like MATCH_RECOGNIZE. Oh, and they also handle structured and semi-structured data amazingly well and all without having to tweak endless knobs. With one solution I have been able to eliminate the need to answer the questions above because Snowflake can very likely handle your use case regardless of the answer. I can go on and on, but trust me, you’ll be satisfied with your Snowflake appetizer. If it’s good enough for Warren Buffett, it’s good enough for me.

But what about Fivetran you say? Well, because you have better things to do than to replicate data from Google Analytics, Salesforce, Square, Concur, Workday, Google Ads, etc. etc. Here’s the full list of current connectors Fivetran supports. Just set it and forget it. No one will give you a metal for mapping data from standard data sources to Snowflake. So just do the simple thing and let’s get to the main dish. 

Main dish: Transforming data

Now that we have all our data sources in Snowflake, what do we do? Well, I haven’t met anyone who doesn’t want to do some level of data quality, documentation, lineage for impact analysis, and do this in a collaborative way that builds trust in the process.

I’ve got you covered. Just use dbt. Yup, that’s it, simple, a single tool that can do documentation, lineage, data quality, and more. dbt is a key component in our DataOps process because it, like Snowflake, just works. It was developed by people who were analysts themselves and appreciated software development best practices like DRY. They knew that SQL is the great common denominator and all it needed was some tooling around it. It’s hard enough finding good analytics engineers let alone finding ones that know Python. Leave the Python to Data Science and first build a solid foundation for your transformation process. Don’t worry, I didn’t forget about your ambition to create great machine learning models, Snowflake has you covered there as well, check out Snowpark.

You will need a little more than dbt in order to schedule your runs and bring some order to what otherwise would become chaos, but dbt will get you a long way there and if you want to know how we solve this with our Datacoves, reach out, we’ll share our knowledge in our 1-hour free consultation.

Dessert: Reporting on data

This three-course meal is quickly coming to an end, but I couldn’t let you go home before you have dessert. You need dashboards, but you also want self-service, then you can’t go wrong with Looker. I am not the only chef saying this, have a look at this.

One big reason for choosing Looker in addition to the above is the fact that version control is part of the process. If you want things that are documented, reused, and follow software development best practices, then you need to have everything in version control. You can no longer depend on the secret recipe that one of your colleagues has on their laptops. People get promoted, move to other companies, forget… and you need to have a data stack that is not brittle. So choose your dessert wisely.

Conclusion 

There are a lot of decisions to be made when creating a great meal. You need to know your guests dietary needs, what you have available, and how to turn raw ingredients into a delicious plate. When it comes to data the options and permutations are endless and most people need to get to delivering solutions so decision makers can improve business results. While no solution is perfect, in my experience there are certain ingredients that when put together well enable users to get to building quickly. If you want to deliver analytics your decision makers can trust, just go Omakase.