A data pipeline mitigates these risks. Pipelines are the tools and processes for moving data from one location to another. A data pipeline’s primary goal is to maintain data integrity as the information moves from one stage to the next. The data pipeline is a critical part of an organization’s growth as the information helps people make strategic decisions using a consistent data set.

Here are the top 10 technologies you need to build a data pipeline for your organization.

What Technologies Are Best for Building Data Pipelines?

A data pipeline is designed to transform data into a usable format as the information flows through the system. The process is either a one-time extraction of data or a continuous, automated process. The information comes from a variety of sources. Examples include websites, applications, mobile devices, sensors, and data warehouses. Data pipelines are critical for any organization to make strategic decisions, execute operations or generate revenue. Data pipelines minimize manual work, automate repetitive tasks, eliminate errors and keep data organized.

1. Free and Open-Source Software (FOSS)

Free and Open-Source Software (FOSS) is, as the name suggests, both free and open-sourced. This means accessing, using, copying, modifying and distributing the code is free.

There are various advantages of using FOSS over proprietary software. For one, FOSS costs less. Secondly, FOSS offers better reliability and more efficient resource usage. The software allows complete control over the code. As a result, FOSS enables companies to customize the software to meet their needs. Many of the technologies listed below fall into this category.

2. MapReduce

MapReduce is an algorithm for breaking large amounts of information into small chunks and processing them in parallel. Hadoop distributes these tasks across many nodes and allows multiple computers to operate simultaneously. Essentially, MapReduce enables the entire cluster to work as one computer.

3. Apache Hadoop

Apache Hadoop is an open-source implementation of the MapReduce programming model on top of Hadoop Distributed File System ( HDFS ). Hadoop provides a framework for the distributed processing of large data sets across many nodes.

4. Apache Pig

When it comes to expressing dataflow programs, Apache Pig is the go-to tool. Pig is a high-level language programming language. The language is well suited to stream processing tasks such as real-time data processing, machine learning, and interactive analysis. Pig implements the MapReduce model by extending the Java language with new operators and functions. These functions make processing complex jobs more efficient.

5. Apache Hive

Apache Hive is an open-source data warehouse system for storing, manipulating, and analyzing unstructured big data stored in Hadoop clusters. Hive extends SQL with a set of operations for manipulating large datasets stored in HDFS using a SQL-like syntax. Hive provides an abstraction over Hadoop’s file system to allow users to interact with HDFS using familiar SQL syntax without needing to be familiar with Hadoop’s programming model or MapReduce programming model.

6. Apache Spark

Apache Spark is a fast-growing technology based on Hadoop. Like Hadoop, Spark is an open-source framework that provides scalable distributed computing capabilities for big data processing.

7. Apache Flume

Apache Flume is a distributed streaming data collection, aggregation, and integration toolkit for Hadoop. Flume enables companies to collect streaming data from many sources into a central location. Flume can be used for things like monitoring systems where it collects metrics from various devices such as routers or switches and stores them in HDFS for analysis by other tools such as Spark or Hive. Flume is also used to collect log files from various systems into HDFS for processing by other tools, such as MapReduce or Pig. Flume provides a simple yet powerful HTTP API for other applications to interact with the central store of data being collected.

8. Amazon Web Services (AWS)

AWS provides a scalable, highly available infrastructure for building data pipelines. AWS offers S3 as a storage service for large volumes of data. S3 is compatible with standard Hadoop file formats. Amazon DynamoDB is a highly scalable database service also used to store large volumes of data in buckets for real-time predictive analysis. Amazon Redshift provides the ability to query large datasets using SQL.

9. Apache Kafka

Apache Kafka is an open-source distributed messaging system designed for high-throughput applications needing reliable real-time communication between distributed applications. Kafka is used in many production environments that require real-time processing for high availability or streaming data from many heterogeneous sources. It is commonly deployed as an application layer service within a Hadoop cluster or on top of other technologies, such as Spark or Kafka.

Kafka offers similar benefits to Hadoop in terms of batch processing. However, Kafka features better scalability as it scales across many machines, making it more suitable for use cases involving large volumes. Kafka has been gaining popularity due to its simplicity and ease of use compared to other solutions.

Kafka also has some additional benefits. For example, Kafka can be used as an event bus and as a message queue with low latency and high throughput. Kafka also handles larger transactions at once. This feature makes Kafka an ideal solution for large-scale batch processing applications.

10. Python

Python is an easy-to-use high-level programming language. Python can be used to write highly-scalable, maintainable and extensible applications. Python can also be used for scripting and automation purposes, such as building websites or automating tasks. Due to its versatility, Python has been gaining popularity recently, especially among web developers.

As organizations become more reliant on data, the need for efficient data processing becomes increasingly important. A data pipeline transforms data into a usable form as it flows through the system. Companies rely on this information for data-backed decision-making.

The post 10 Technologies You Need To Build Your Data Pipeline appeared first on Pragmatic Institute - Resources.

Product professional and instructor Will Scott talks about product innovation, B2B innovation, and how companies can balance the need to innovate with the need to grow.

in·no·vate

/ˈinəˌvāt/

verb

• make changes in something established, especially by introducing new methods, ideas, or products. “the company’s failure to diversify and innovate competitively”
• introduce (something new, especially a product). “innovating new products, developing existing ones”

I was teaching a class the other day and ended up in an engrossing conversation about the expansive topic of innovation. This got me thinking more about the nature of innovation, specifically how the innovation paths that a company chooses to follow are intimately related to how that company intends to grow. 

A company’s growth strategy, which outlines its plans for expansion and market dominance, doesn’t operate in isolation. It is also deeply intertwined with its product innovation strategy, its blueprint for creating and driving adoption of its novel solutions.  

As B2B companies chart their path forward, the choices they make in pursuit of growth invariably shape the way they approach product innovation, and vice versa. There is an intricate relationship between expansion and innovation strategies. The drive for expansion shapes and sometimes constrains innovation strategies, while disruptive innovation ideas can open new avenues for growth. 

You can start from the beginning or skip the to section that interests you the most:

• Platform-led growth
• Sales-led growth
• Product-led growth
• Partner-led growth

Finding Alignment Between Expansion and Invention

How a B2B company chooses to go to market and grow its market share is of course a strategic decision based on the investment hypothesis behind a company and its capabilities. In fact, I would say that growth strategies precede innovation strategies.  Looking at the most common growth strategies I see the following innovation vectors that are most aligned with a particular growth strategy. 

We should note here that we live in the real world of grey where bright lines are the stuff of theory and academia. We also live in a world where it’s not a matter of “either/or” but that of “both/and”. So with this premise in mind, the following are by no means mutually exclusive. Indeed, upon closer inspection many precedents, antecedents and complimentary connections reveal themselves. 

Platform-led Growth

Platform-led growth is a strategy where a company offers a foundational platform for other businesses to build their products and services. This approach allows these businesses to reach their end customers more effectively. 

Examples of Platform-led Growth

Notion: By offering a versatile workspace platform, Notion enables businesses and individuals to customize and build upon its features. This leads to wider adoption and increased reliance on its platform.
Salesforce: As a leading CRM platform, Salesforce allows third-party developers to create applications within its ecosystem. This expands its reach and solidifies its central role in business operations. 

Strengths of Platform-led Growth

Customers tend to become more loyal and engaged due to the integrated solutions provided by the platform. This enhances its overall stickiness in the market. 

In the realm of platform-led growth, the expansion of the ecosystem is a significant strength, paving the way for a diverse range of services and offerings. This growth strategy often enjoys the benefit of recurring revenue streams, thanks to third-party developers who build upon the platform. Furthermore, customers tend to become more loyal and engaged. This is due to the integrated solutions provided by the platform, enhancing its overall stickiness in the market. 

Weaknesses of Platform-led Growth

This approach to growth is not without its challenges. Relying on third-party developers can sometimes result in inconsistent quality across the platform’s offerings. Maintaining the platform and ensuring compatibility with a myriad of third-party solutions can also pose significant risks. A poor experience with an application built on a platform will often reflect poorly on the platform vendors themselves. 

Available Innovation Vectors 

A modular architecture becomes indispensable, allowing for a wide variety of integrations. To thrive in a platform-led growth environment, certain product innovations are crucial. Open APIs are essential as they facilitate the development efforts of third parties. A modular architecture becomes indispensable, allowing for a wide variety of integrations. Lastly, to provide users with a smooth and cohesive experience, robust integration capabilities are a must. 

Sales-led Growth

Sales-led growth is a well-trodden path for the B2B companies. This strategy emphasizes direct sales efforts, often using a dedicated sales team to approach potential clients, especially in the enterprise software domain. 

Examples of Sales-led Growth

Oracle: Oracle’s vast suite of enterprise solutions is primarily driven into the market through its robust sales teams. This ensures deep penetration into businesses for Oracle solutions.
EMC: EMC’s growth in storage solutions was propelled by its direct sales force, establishing strong relationships with enterprise clients 

Strengths of Sales-led Growth

Sales-led growth allows for tailored pitches to potential clients, fostering deep relationships and ensuring that the product meets specific client needs. This approach often results in high deal sizes, especially in the enterprise space. 

Weaknesses of Sales-led Growth

Growth rate might be limited by the scalability of the sales team, and it can be slower to adapt to market changes than more agile strategies. It is also resource-intensive, requiring significant investment in and dependence on sales personnel and training. Moreover, the growth rate might be limited by the scalability of the sales team. It can also be slower to adapt to market changes than more agile strategies. 

Available Innovation Vectors 

For sales-led growth, a significant direction in product innovation should pivot towards customization or the illusion of customization through configuration. By closely listening to direct feedback from the sales team, businesses can develop features or modules tailored to the unique needs of specific clients.  

Another critical avenue is the integration of products with other enterprise solutions. Such integrations can be a compelling selling point, enhancing the product’s appeal during sales pitches. Lastly, this innovation strategy should emphasize investments in sales enablement tools, such as advanced CRM systems, insightful sales analytics and interactive product demos, to further empower and equip the sales team. 

Product-led Growth

When it comes to product-led growth, the product itself is the primary driver of the B2B company’s growth. Its ability to get the job done and user experience lead to organic adoption and customer retention. With product-led growth, the user often gets an initial experience in limited free functionality, teasing them with the promise of exponential utility through moving to incremental tiers of (paid) offerings. 

Examples of Product-led Growth

Zoom: The simplicity and reliability of Zoom’s video conferencing tool led to widespread organic adoption, especially during the remote work surge.
Trello: Trello’s intuitive board-based task management system attracts teams and individuals, promoting natural growth through its utility. 

Strengths of Product-led Growth

The product-centric growth strategy allows for organic expansion due to its inherent utility and user experience. This often reduces the cost of customer acquisition and leads to higher customer loyalty. 

Weaknesses of Product-led Growth

It’s highly reliant on the product’s quality and continuous improvement. If competitors offer a better product or if the user’s needs change, adoption rates can decrease quickly. This is especially true if the real and perceived switching costs are seen as minimal. 

Available Innovation Vectors 

For a product-centric growth strategy, a relentless focus on iterative improvements is crucial, enabling the product to adapt to usage data and shifting market dynamics. 

Furthermore, a thoughtful approach toward establishing integrations with complementary products and fostering an ecosystem can significantly enhance the core product’s utility. This makes a product even more indispensable to its users. 

Finally, an explicit intention to create affinity by increasing those perceived switching costs can minimize the rate at which customers churn to other competing alternatives. 

Partner-led Growth

Partner-led growth is a strategy that leverages partnerships and a network of value-add resellers to expand market reach. 

Examples of Partner-led Growth

Cisco: Cisco’s extensive partner network, including resellers and integrators, amplifies its reach in the networking and tech industry.
SAP: SAP’s ecosystem of partners ensures that its enterprise solutions are integrated and adopted across various industries and scales. 

Strengths of Partner-led Growth

Partner-led growth capitalizes on the strengths and market reach of partners. This allows for rapid scale and penetration into new markets without significant upfront investments. 

Weaknesses of Partner-led Growth

There’s a dependency on partners for growth, which can sometimes result in reduced control over the customer experience. Additionally, revenue splits or partner incentives can erode profit margins. 

Product Innovation Vectors 

In the realm of Partner-Led Growth, it’s essential to invest in creating scalable and user-friendly integration tools. These tools enable partners to seamlessly integrate and offer the product to their existing customer base. Additionally, B2B companies should prioritize the development of comprehensive training materials and certification programs, ensuring that partners have the expertise to represent the product accurately and effectively. A focus on collaborative marketing strategies can also be fruitful, co-developing go-to-market strategies and promotional campaigns with partners to harness their local insights and reach. 

Balancing innovation and expansion is crucial for sustained growth in B2B companies. By aligning product innovation with strategic goals, companies can drive growth while continuously meeting evolving customer needs. 

The post How B2B Companies Can Balance Innovation and Expansion appeared first on Pragmatic Institute - Resources.

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“The best CX is often when you can meet your customers exactly where they are. For example, if your customer persona is busy and unable to engage with email easily, offering phone support likely brings happier customers and retention.”
– Erin Knobler, Customer Experience (CX) Guru

In this episode of Product Chats, Erin Knobler, CX researcher and designer, joins us to discuss the importance of customer experience (CX), the methods for gathering data and insights about CX, and the role CX plays in achieving business goals.

Key Takeaways:
Takeaway 1: Understanding the customer journey, which involves mapping out each touchpoint a customer has with a business, is crucial to identifying areas where customers may be experiencing difficulties.
Takeaway 2: Research and data, from customer-facing employees, reviews, customer support interactions, and other sources, provide valuable insights into customer behavior and potential issues.
Takeaway 3: Conducting customer interviews provides direct insights into the customer’s experience and can reveal problems that may not be apparent from data alone.

ABOUT OUR GUEST:
Erin Knobler is a seasoned CX researcher and designer. She has extensive experience in leveraging data to transform product strategies and optimize workflows. Her expertise lies in turning qualitative data into quantifiable insights that drive customer-led growth. Erin’s passion for customer experience and her ability to bring together business goals and customer needs makes her a valuable voice in the industry.

ABOUT PRAGMATIC INSTITUTE:
Since 1993, we’ve provided training and professional development for product, design, and data professionals to accelerate innovation, increase customer satisfaction, and bolster revenue. Through our comprehensive methods and propriety Pragmatic Framework, we train professionals with the skills needed to accelerate their product management and product marketing careers, increase team efficiency, and boost revenue.

Our online, in-person, and on-demand courses are taught by industry experts with decades of experience at top companies like Microsoft, Cisco, Dell, Hallmark, Quaker Oats, and Dun & Bradstreet. We offer individual course training in product, design and data, and certification tracks for Product Management Certifications, Product Marketing Certifications, and the Pragmatic Product Master Certification. All attendees gain access to the Pragmatic Alumni Community, a network of over 30,000 alumni worldwide.

If you’re looking for product management or product marketing training or are pursuing a product marketing certification or product management certification, browse our certification paths at https://www.pragmaticinstitute.com/product/. Courses are available online, in-person, or on-demand to fit your schedule.

Want to learn more about becoming a Pragmatic Certified Product Manager? Learn more here.

#ProductManagement #ProductManager #ProductManagementCertification #ProductManagementStrategy #PragmaticInstitute

The post Transforming Business Goals with Customer Experience Insights: An Interview with Erin Knobler appeared first on Pragmatic Institute - Resources.

Data is being collected with every click, scroll, and choice we make both online and in our everyday lives. This data can reveal habits, preferences, and trends that companies can leverage to boost their profits and drive success. It also reveals the increasing importance of data and the need for companies to find better ways to collect, analyze and utilize data to gain a competitive edge.

One way that companies are leveraging data to their advantage is through data partnerships. In the following article we are going to explore what data partnerships are along with the benefits and challenges that go along with the territory. 

What is a data partnership? 

A data partnership is a collaborative arrangement between two or more companies in which data and resources are shared. The goal of a data partnership is to achieve shared goals, improve decision making process, create new business opportunities or scale. These partnerships can take many forms, ranging from simple data-sharing agreements to more complex joint ventures.

Data partnerships can be arranged between any organization that finds value in the arrangement, from businesses and research institutions to government organizations. Let’s look closer at the benefits and challenges of data partnerships.

Benefits of Data Partnerships Between Companies

While there are many benefits to data partnerships, one of the most significant benefits is having access to a wider pool of data and resources. By working with other companies, businesses can quickly  tap into a wealth of knowledge and expertise that would otherwise be unavailable to them. This can be particularly valuable for smaller companies that may not have the resources or capabilities to collect and analyze data on their own.

In addition to accessing new sources of data, data partnerships can also help companies achieve economies of scale. By pooling resources and working together, businesses can reduce costs and increase efficiency. For example, two companies that are both collecting and analyzing data on their own may be able to significantly reduce their expenses by consolidating their efforts and sharing the resulting data.

Data partnerships can also be beneficial for companies looking to better understand their customers and markets. By sharing data and insights, businesses can gain a more comprehensive view of their target audience. In turn, this information is useful in developing more effective marketing and sales strategies. This can be particularly useful in fast-changing markets where it is important to stay on top of evolving consumer trends and preferences.

 Challenges to Successful Data Partnerships

Data partnerships also present a number of challenges, one of the largest being ensuring that the shared data is accurate, reliable, and protected. Additionally, companies must be careful to maintain the privacy and security of their own data, as well as that of their partners. This can be particularly challenging in cases where the data being shared is sensitive or confidential. This creates legal and compliance issues that must be carefully followed.

Another challenge is managing the relationship between the partners. For example, it is important for companies to establish clear terms and conditions for the data partnership. This requires strong communication and having clear collaboration processes in place. Many factors can make this challenging. For example, simply being in different regions or different industries can make this more difficult.

Despite the challenges, data partnerships can be a powerful tool for companies looking to gain a competitive edge. By sharing data and resources, businesses can improve their decision-making, optimize their operations, and drive innovation. 

Data Partnership Success Stories 

Let’s explore examples of successful data partnerships through a variety of industries. 

Data partnership between retail and tech 

Walmart entered into a data partnership with the Chinese tech giant JD.com. Through this partnership, Walmart was able to access JD’s vast pool of data on consumer behavior in China. In turn, this helped Walmart better understand the Chinese market and develop more targeted marketing and sales strategies. 

Data partnership between healthcare organizations

In the healthcare industry, data partnerships are common. The health insurance company UnitedHealth Group partnered with the analytics firm Optum to create a new data-driven platform for population health management. Through this partnership, UnitedHealth and Optum were able to combine their expertise and data to develop new tools and approaches for improving the health outcomes of patients.

Data partnerships between finance organizations

In the finance industry, Visa partnered with the data analytics firm FICO to improve fraud detection and prevention. By sharing data and resources, Visa was able to leverage FICO’s expertise in analytics and machine learning to develop more sophisticated fraud detection algorithms. This partnership has helped Visa to reduce fraudulent transactions and improve the security of its payment network.

Conclusion 

Data partnerships are becoming an increasingly popular way for companies to leverage data and resources to gain a competitive advantage. By working together, businesses can access a wider pool of data, achieve economies of scale, and better understand their customers and markets. While there are challenges to be managed, the potential benefits of these partnerships make them worth considering for any company looking to stay competitive. 

Resources 

Business-Driven Data Analysis

Data Science for Business Leaders

Getting Executive Buy-In for Data Projects

The post How Data Partnerships Can Help Your Company Scale appeared first on Pragmatic Institute - Resources.

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What do you need in a business plan? Numbers, figures, financial plans and a few ideas? Leadership teams need more than numbers to make strategic decisions that matter. Yet, many organizations rush to an evaluation of financial forecasts and revenues before an idea has even been clearly defined.

Ideas are easy—it’s the execution that’s hard. Watch our recent webinar as we discuss the importance of finding a way to focus and filter your ideas to identify the best opportunity for your business plan. We’ll show you the best ways to triage and grade your product ideas, build a useful business plan, and make sure your metrics line up with your goals.

Steve Johnson, author, speaker, former Pragmatic Institute instructor and current vice president of products at Pragmatic Institute, explores:

• Using a product canvas as a business plan
• Prioritizing and re-prioritizing product ideas
• Aligning product metrics with goals

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Watch our recent webinar, as we discuss the importance of gathering competitive intelligence at each step of the process to successfully launch a new product. We’ll look at how to utilize competitive research to validate, plan, execute and promote a new product through the entire launch process. We’ll follow a product launch timeline, laying out key milestones to include competitive analysis and examining how to act upon feelings to successfully bring a new product to market.

Ellie Mirman, CMO of Crayon, and Bryan Dunn, VP of Product at Crayon, discuss:

• How to validate ideas and identify potential value using competitive intelligence
• How to correctly position your new product in the market
• Where competitive analysis factors into each step of the launch process

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