What Drives Your Organization to be Data-driven?
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Every organization has its own unique data culture and capabilities. Yet each is expected to use technology trends and solutions in the same way as everyone else. Your organization may be built on years of legacy applications, you may have developed a considerable amount of expertise and knowledge, yet you may be asked to adopt a new approach based on a technology trend. On the other hand, you may be on the other side of the spectrum, a digitally native organization built with engineering principles from scratch without legacy systems but expected to follow the same principles as process driven, established organizations. The question is, should we treat these organizations in the same way when it comes to data processing? In this series of blogs and papers this is what we are exploring: how to set up an organization from the first principles from data analyst, data engineering and data science point of view. In reality, there is no such organization that is solely driven by one of these but it is likely to be a combination of multiple types. What type of organization you become is then driven by how much you are influenced by each of these principles.
When you are considering what data processing technology encompasses, take a step back and make a strategic decision based on your key goals. This can be whether you optimize for performance, cost, reduction in operational overhead, increase in operational excellence, integration of new analytical and machine learning approaches. Or perhaps you’re looking to leverage existing employees’ skills while meeting all your data governance and regulatory requirements. We will be exploring these different themes and will focus on how they guide your decision-making process. You may be coming from technologies which are solving some of the past problems and some of the terminologies may be more familiar, however they don’t scale your capabilities. There is also the opportunity cost of prioritizing legacy and new issues that arise from a transformation effort, and as a result your new initiative can set you further behind on your core business while you play catch up to an ever changing technology landscape.
Data value chain
The key for any ingestion and transformation tool is to extract data from a source and start acting on it. The ultimate goal is to reduce the complexity and increase the timeliness of the data. Without data, it is impossible to create a data driven organization and act on the insights. As a result, data needs to be transformed, enriched, joined with other data sources, and aggregated to make better decisions. In other words, insights on good timely data mean good decisions.
While deciding on the data ingestion pipeline, one of the best approaches is to look into the volume of data, the velocity of the data, and type of data that is arriving. Other considerations include the number of different data sources you are managing, whether you need to scale to thousands of sources using generic pipelines, whether you want to create one generic pipeline but then apply data quality rules and governance. ETL tools are ideal for this use case as generic pipelines can be written and then parameterized.
On the other hand, consider the data source. Can the data be directly ingested without transforming and formatting the data? If the data does not need to be transformed and can be ingested directly into the data warehouse as a managed solution. This not only reduces the operational costs but also allows for more timely data delivery. If the data is coming in through an unstructured format such as XML or in a format such as EBCDIC and needs to be transformed and formatted, then a tool with ETL Capabilities can be used depending on the speed of the data arrival.
It is also important to understand the speed and time of arrival of the data. Think about your SLAs and time durations/windows that are relevant for your data ingestion plans. This would not only drive the ingestion profiles but would also dictate which framework to use. As discussed above, velocity requirements would drive the decision-making process.
Type of Organization
Different organizations can be successful by employing different strategies based on the talent that they have. Just like in sports, each team plays with a different strategy with the ultimate goal of winning.
Organizations often need to decide on what’s the best strategy to take in respect to data ingestion and processing – whether you need to hire an expensive group of data engineers, or exploit your data wizards and analysts to enrich and transform data that can be acted on, or whether it would be more realistic to train the current workforce to do more functional/high value work rather than to focus on building generally understood and available foundational pieces.
On the other hand, the transformation part of ETL pipelines as we know it, dictates where the load will be. All of these are made a reality in the cloud native world where data can be enriched, aggregated, and joined. Loading data into a powerful and modern data warehouse means that you can already join and enrich the data using ELT. Consequently, ETL isn’t really needed in its strict terms anymore if the data can be loaded directly into the data warehouse.
All of the above was not possible in the traditional, siloed, and static data warehouses and data ecosystems whereby systems would not talk to each other or there were capacity constraints in respect to both storing and processing the data in the expensive Data Warehouse. This is no longer the case in the BigQuery world as storage is now cheap and transformations are now much more capable without constraints of virtual appliances.
If your organization is already heavily invested into an ETL tool, one option is to use them to load BigQuery and transform the data initially within the ETL tool. Once the as-is and to-be are verified to be matching, then with the improved knowledge and expertise one can start moving workloads into BigQuery SQL, and effectively do ELT.
Furthermore, if your organization is coming from a more traditional data warehouse that extensively relies on stored procedures and scripting, then the question that one may ask is, do I continue leveraging these skills and expertise and use these capabilities that are also provided in BigQuery? ELT with BigQuery is more natural, similar to what’s already in Teradata BTEQ, Oracle PL/SQL but migrating from ETL to ELT requires changes. This change then enables exploiting streaming use cases, such as real-time use cases in retail. This is because there is no preceding step before data is loaded and made available.
Organizations can be broadly classified under 3 types as Data Analyst Driven, Data Engineering driven, and Blended organization. We will be covering a Data Science driven organization within the Blended category.
Data Analyst Driven
Analysts understand the business and are used to using SQL/spreadsheets. Allowing them to do advanced analytics through interfaces that they are accustomed to enables scaling. As a result, easy to use ETL tooling to bring data quickly into the target system becomes a key driver. Ingesting data directly from a source or staging area then also becomes critical as it allows analysts to exploit their key skills using ELT and increases timeliness of the data. This is commonplace with traditional EDWs and realized by extended capabilities of using Stored Procedures and Scripting. Data is enriched, transformed, and cleansed using SQL and ETL tools act as the orchestration tools.
The capabilities brought by cloud computing on separation of data and computation changes the face of the EDW as well. Rather than creating complex ingestion pipelines, the role of the ingestion becomes, bringing data close to the cloud, staging on a storage bucket or on a messaging system before being ingested into the cloud EDW. This then releases data analysts to focus on looking into data insights using tools and interfaces that they are accustomed to.
Data Engineering / Data Science Driven
Building complex data engineering pipelines is expensive but enables increased capabilities. This allows creating repeatable processes and scaling the number of sources. Once complemented with cloud it enables agile data processing methodologies. On the other hand, data science organizations allow carrying out experiments and producing applications that work for specific use cases but are not often productionised or generalized.
Real-time analytics enables immediate responses and there are specific use cases where low latency anomaly detection applications are required to run. In other words, business requirements would be such that it has to be acted upon as the data arrives on the fly. Processing this type of data or application requires transformation done outside of the target.
All the above usually requires custom applications or state-of-the-art tooling which is achieved by organizations that excel with their engineering capabilities. In reality, there are very few organizations that can be truly engineering organizations. Many fall into what we call here as the blended organization.
Blended org
The above classification can be used on tool selection for each project. For example, rather than choosing a single tool, choose the right tool for the right workload, because this would reduce operational cost, license cost and use the best of the tools available. Let the deciding factor be driven by business requirements: each business unit or team would know the applications they need to connect with to get valuable business insights. This coupled with the data maturity of the organization would be the key to making sure the right data processing tool would be the right fit.
In reality, you are likely to be somewhere on a spectrum. Digital native organizations are likely to be closer to being engineering driven, due to their culture and business that they are in. However, brick and mortar organizations would be closer to being analyst driven due to the significant number of legacy systems and processes they possess. These organizations are either considering or working toward digital transformation with an aspiration of having a data engineering / software engineering culture like Google.
The blended organization with strong skills around data engineering, would have built the platform and built frameworks, to increase reusable patterns would increase productivity and then reduce costs. Data engineers focus on running Spark on Kubernetes whereas infrastructure engineers focus on container work. This in turn provides unparalleled capabilities as application developers focus on the data pipelines and even the underlying technologies or platforms changes code stays the same. As a result, security issues, latency requirements, cost demands and portability are addressed at multiple layers.
Conclusion – What type of organization are you?
Often an organization’s infrastructure is not flexible enough to react to a fast changing technological landscape. Whether you are part of an organization which is engineering driven or analyst driven, organizations frequently look at technical requirements that inform which architecture to implement. But a key, and frequently overlooked, component needed to truly become a data-driven organization is the impact of the architecture on your data users. When you take into account the responsibilities, skill sets, and trust of your data users, you can create the right data platform to meet the needs of your IT department as well as your business.
To become a truly data-driven organization, the first step is to design and implement an analytics data platform that meets your technical and business needs. The reality is that each organization is different and has a different culture, different skills, and capabilities. Key is to leverage its strengths to stay competitive while adopting new technologies when it is needed and as it fits to your organization.
To learn more about the elements of how to build an analytics data platform depending on the organization you are, read our paper here.
Is it Cheaper to Run Oracle Workloads on Google Cloud Than On-Prem? Yes, By 78%
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On-premises relational database management systems (RDBMS) are the traditional heart of many core business operations, but cloud-based relational databases provide the agility and scalability of the cloud while eliminating many of the onerous tasks associated with maintaining your own infrastructure.
To find out how much more value cloud-based relational databases provide, research firm ESG compared an on-premises RDBMS strategy against hosting the same data on one of Google Cloud’s database offerings, called Cloud Spanner.
Its analysis showed that hosting the same data using Google Cloud Spanner is 78% less expensive than using on-premises servers–and up to 37% less expensive than using other cloud databases.
Here’s a quick snapshot of where those benefits stem from:
To read the full report and discover how Google Cloud Spanner ranked against other cloud-based database offerings, download ESG’s report, Analyzing the Economic Benefits of Google Cloud Spanner Relational Database Service.
Groww’s Google Cloud-Powered Platform: The Key to Secure and Successful Investing
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Groww makes investments simple and accessible, using Google Kubernetes Engine to ensure a reliable platform for customers, and makes data-backed decisions to grow its business with BigQuery.
About Groww
Headquartered in Bangalore, Groww is India’s fast-growing online investment platform that offers a simple and easy way to invest in stocks, direct mutual funds, IPOs, ETFs, and digital gold. Its mission is to make investing as intuitive and accessible as ecommerce.
Google Cloud results
- Reduces hardware costs with Preemptable Virtual Machines
- Enables a lean DevOps team with Google Cloud
- Analyzes data effectively and quickly for agile business growth
Investing is one way to ensure financial security. However, the thought of it can be a daunting one, especially for people without any prior experience. With a mission to make investment simple for digital natives in India, Groww was launched in 2016.
“We noticed that many people were on social media, booking cabs, and ordering food online, but the same people were not investing, despite having the means to do so,” says Singh. This observation led to a lightbulb moment for the team, and they realized that in order to appeal to the millennial, mobile-savvy generation, they had to create an investment platform that was as easy to use as an ecommerce platform.
Managing unpredictable spikes with Google Kubernetes Engine
As with any platform, there are bound to be peak and non-peak hours when it comes to traffic. For Groww, regular spikes take place in the early mornings, or in the evenings when people are more relaxed having come home from work. But the nature of the fintech industry is a volatile one. Investors are only human, and their investment decisions can be swayed quickly by the news. As such, spikes in traffic can happen at the most unpredictable times. To cope with this unpredictability, Groww uses Google Kubernetes Engine to scale up and down automatically to meet the required capacity around the clock. This also helps the company save costs, as it pays only for what is needed.
“No matter how much of an expert you are, you can never predict when traffic on the platform will be heavy,” says Singh. “Google Kubernetes Engine helps ensure that we never run out of capacity, without overspending on infrastructure cost.”
More recently, the investment company started using Preemptible Virtual Machines, which run at one third of its hardware cost. It also leverages Anthos to monitor and manage its backend infrastructure and to have better workload visibility. Singh shares, “We are very open to adopting new technologies, and our team is always eager to learn if we can do things better. We believe that technology is always evolving and it’s our responsibility to learn and make use of what’s available out there.”
Despite having so much running in the background, Singh explains that the company keeps a very lean DevOps team. “We’ve only got four or five people in DevOps, and that’s only possible because Google Cloud products are already able to run on their own.”
Making swift, data-backed business decisions
Infrastructure is only part of the equation for a successful business. Outside of operations, the ability to analyze data effectively is arguably the most important component for a startup to thrive. Groww leverages BigQuery to make decisions quickly and efficiently. “With BigQuery, we have a place where we can put all data, fire queries, and build dashboards almost instantly, allowing us to make business decisions quickly,” explains Singh.
The team also uses Looker Studio to clearly visualize the information generated through charts and graphs. The best part? Groww doesn’t need to spend additional time and resources setting up a large data team, since BigQuery does most of the work and in a shorter period of time. The resources saved also enables the team to focus on addressing functional requirements, rather than managing and sizing the data platform.
“From a startup perspective, BigQuery is really helpful because often setting up your own data lake can be very costly, and a distraction when the team is also busy focusing on setting up an infrastructure,” adds Singh.
Ensuring security and compliance with Google Cloud
As a fintech company, security and compliance continue to be top priorities for Groww. It chose Google Cloud as its preferred cloud provider because there are three data center replication zones in Mumbai, which means it adheres to financial regulations for keeping its user data within borders.
Moving forward, Groww plans to evolve its platform alongside its users. Singh says, “As we gain more users with different wants and needs, it will be a natural progression that the company evolves. I believe that with Google Cloud, we are well equipped to pave the way to the future.”
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IDC recently examined the benefits of implementing BigQuery for SAP data. The findings reveal massive improvements to the SAP customers’ overall business results due to faster access to data insights, lower data warehouse operation cost, and increased productivity among the data warehouse and development teams. Download the report now!
Transforming Software Development Education with CourseMatix
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As the world increasingly relies on software, businesses have struggled to find enough developer talent to build and maintain their applications. IDC estimates the global shortage of software developers reached 1.4 million in 2021 and expects that number to balloon to 4 million by 2025.
Higher education is a great starting point to solve the software development skills shortage. One institution — Spokane Community College — has made strides in enhancing student learning and serving more students with the right mix of software development and IT education while saving educators time.
Building a modern IT education platform
As a first step to improve computer science education and scale the number of students it could teach, Spokane Community College needed a platform that could help instructors more efficiently manage the teaching process. That’s where CourseMatix came in.
CourseMatix is an education platform that delivers the required tools, platforms, and frameworks such as automated grading, assignment tracking, and testing to save teachers time and enhance students’ learning.
CourseMatix is entirely made up of microservices, with significant compute demands because it creates and runs a unique dedicated environment for each student. The platform is complex by nature as it supports a range of technologies that computer science professors need to teach varying coursework. This led to issues with scalability, reliability, and uptime when running on legacy infrastructure.
“We really wanted to no longer manage our infrastructure, leaving us to focus entirely on our platform,” says Aleks Korn, chief executive officer of CourseMatix. “We engaged Google Cloud partner CDW to see how we could simplify and improve our IT environment. The decision was quickly made to migrate to Google Kubernetes Engine (GKE).”
GKE provides CourseMatix with automatic horizontal and vertical scaling to minimize costs and downtime, while removing management and maintenance requirements. This enables CourseMatix to expand the reach of its platform at the lowest possible price and maintain a lean team. CDW helped CourseMatix make this decision to migrate to GKE, managed the migration, and provided guidance on a storage solution.
Because CourseMatix has high data storage volume requirements (given the size of software development assignments) and needs to be fully reliable to ensure work is not lost, it also chose to work with NetApp. NetApp Cloud Volumes Service for Google Cloud perfectly complements GKE in its highly expandable, reliable, and user-friendly capabilities. The platform was now ready for its U.S. launch at Spokane Community College.
Transforming software development education
Spokane Community College needed a solution to address the challenges facing its professors, particularly with respect to time management.
“Each software development assignment can take an hour to grade,” says Bret Dickey, professor of software development at Spokane Community College. “When you have 100 students, you’re talking weeks of turnaround time to grade those projects. CourseMatix provides immediate feedback, which is a game changer for us as professors and our students.”
Students now complete and submit their assignments with real-world tools, using source control, creating branches to complete their work, and merging those branches to push up to the server where pipelines run the applications.
“It’s making our teaching better overall, including using real-world concepts to enhance teaching and outcomes,” says Dickey.
CourseMatix also provides the tooling to upskill on various programming languages and software packages required for coursework, helping Spokane Community College manage server-side frameworks and client-side languages that each have their own tools.
“Students can access their cloud-based coding environment and all the tools they may need for an assignment are ready to go,” says Karmen Blake, another software development professor at Spokane Community College. “Having a central system for tooling within CourseMatix has been a huge time saver, meaning more time for learning and practice that prepares students for careers as professional software developers.”
Karmen and Dickey have seen marked improvements in their ability to teach and in student learning by using CourseMatix. For example, the platform provides automated hints and varying error messages based on the number of attempts a student has made to solve a problem to help each student along without making the assignment too easy or too difficult.
As a cloud-based platform, CourseMatix also empowers students from a wide range of socioeconomic backgrounds to learn computer science.
“As long as students can access the internet on their laptops, they have everything to complete their assignments,” says Karmen. “The cloud-based CourseMatix offering improves efficiency and equity across all students in our computer science programs.”
With a more fluid experience for students and immense amounts of time saved on grading, Spokane Community College is in a better position to increase enrollment in software development classes.
“Now that we have automated our grading and optimized our assignment processes, I am beginning to take on more students and teach more classes,” says Dickey. “I could not have imagined this outcome only a couple of years ago, and it’s all possible thanks to CourseMatix.”
To learn more about how partners help organizations make the most of Google Cloud, you can visit our partner page.
An AI-Powered Cost Cutting Guide: 8 Strategies for Maximizing Profits
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We are increasingly seeing one question arise in virtually every customer conversation: How can the organization save costs and drive new revenue streams?
Everyone would love a crystal ball, but what you may not realize is that you already have one. It’s in your data. By leveraging Data Cloud and AI solutions, you can put your data to work to achieve your financial objectives. Combining your data and AI reveals opportunities for your business to reduce expenses and increase profitability, which is especially valuable in an uncertain economy.
Google Cloud customers globally are succeeding in this effort, across industries and geographies. They are improving ROI by saving money and creating new revenue streams. We have distilled the strategies and actions they are implementing—along with customer examples and tips—in our eBook, “Make Data Work for You.” In it, you’ll find ways you can pare costs, increase profitability, and monetize your data.
Find money in your data
Our Google Cloud teams have identified eight strategies that successful organizations are pursuing to trim expenses and uncover new sources of revenue through intelligent use of data and AI. These use cases range from scaling small efficiencies in logistics to accelerating document-based workflows, monetizing data, and optimizing marketing spend.
The results are impressive. They include massive cost savings and additional revenue. On-time deliveries have increased sharply at one company, and procure-to-pay processing costs have fallen by more than half at another. Other organizations have reaped big gains in ecommerce upselling and customer satisfaction.
We’ve found that businesses across every industry and around the globe are able to take action on at least one of these eight strategies. Contrary to common misperceptions, implementation does not require massive technology changes, crippling disruption to your business, or burdensome new investments.
What success looks like
If you worry your business is not ready or you need to gain buy-in from leadership, the success stories of the 15 companies in this report are helpful examples. Learning how organizations big and small, in different industries and parts of the world, have implemented these data and AI strategies makes the opportunities more tangible.
Carrefour
Among the world’s largest retailers, Carrefour operates supermarkets, ecommerce, and other store formats in more than 30 countries. To retain leadership in its markets, the company wanted to strengthen its omnichannel experience.
Carrefour moved to Google Data Cloud and developed a platform that gives its data scientists secure, structured access to a massive volume of data in minutes. This paved the way for smarter models of customer behavior and enabled a personalized recommendation engine for ecommerce services.
The company saw a 60% increase in ecommerce revenue during the pandemic, which it partly attributes to this personalization.
ATB Financial
ATB Financial, a bank in the Canadian province of Alberta, uses its data and AI to provide real-time personalized customer service, generating more than 20,000 AI-assisted conversations monthly. Machine learning models enable agents to offer clients real-time tailored advice and product suggestions.
Moreover, marketing campaigns and month-end processes that used to take five to eight hours now run in seconds, saving over CA$2.24 million a year.
Bank BRI
Bank BRI, which is owned by the Indonesian government, has 75.5 million clients. Through its use of digital technologies, the institution amasses a lot of valuable data about this large customer base.
Using Google Cloud, the bank packages this data through more than 50 monetized open APIs for more than 70 ecosystem partners who use it for credit scoring, risk management, and other applications. Fintechs, insurance companies, and financial institutions don’t have the talent or the financial resources to do quality credit scoring and fraud detection on their own, so they are turning to Bank BRI.
Early in the effort, the project generated an additional $50 million in revenue, showing how data can drive new sources of income.
How to get going now
“Make Data Work for You” will help you launch your financial resiliency initiatives by outlining the steps to get going. The process lays the groundwork for realizing your own cost savings and new revenue streams by leveraging data and AI.
Among these steps include building frameworks to operate cost efficiently, make informed decisions related to spending and optimize your data and AI budgets.
Operate: Billing that’s specific to your use-case
Control your costs by choosing data and analytics vendors who offer industry-leading data storage solutions and flexible pricing options. For example, multiple pricing options such as flat rate and pay-as-you-go allow you to optimize your spend for best price-performance.
Inform: make informed decisions based on usage
Use your cloud vendor’s dashboards or build a billing data report to gain insights on your spending over time. Make use of cost recommendations and other forecasting tools to predict what your future expenses are going to be.
Optimize: Never pay more than you use
While planning data analytics capacity, organizations often overprovision and overpay than what they actually use. Consider migrating your workloads that have unpredictable demand to a data warehousing solution that offers granular level autoscaling features so that you never have to pay for more than what you use.
There are other key moves that will set your initiative up for success including how to shorten time to value in building AI models and measuring impact. You can find details in the report.
A brighter future
The teams at Google Cloud helped the companies in “Make Data Work for You,” along with many more organizations, use their data and AI to achieve meaningful results. Download the full report to see how you can too.
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