Making data analytics work: Building a data-driven organization

Summary

The transcript emphasizes the importance of organizational design, people, and processes alongside traditional technology hardware and software for effective big data analytics. It highlights the need for data hygiene, governance, and a service bureau culture to ensure analytics are useful to internal customers. The speaker suggests focusing on creating a service bureau culture before deciding on centralization or decentralization. Key roles such as data scientists, business solution architects, campaign experts, and advanced modelers are crucial for leveraging big data effectively, ensuring models are actionable and results-driven.

Takeaways

• 🛠️ The script emphasizes the importance of traditional capabilities such as technology hardware, software, and applications in building infrastructure for big data and analytics.
• 🏗️ It highlights the need for building capabilities around organizational design, people, and processes to effectively utilize big data and analytics.
• 🧑‍🔬 The role of analysts and data scientists is crucial, and it's important to determine the type of expertise required, whether it's just analysts or more specialized roles like data scientists or business solution architects.
• 🔍 The script stresses the importance of data quality, stating that 'garbage in, garbage out' still applies, and that data hygiene and cleansing are vital for achieving accurate results from analytics models.
• 🗃️ Data governance is essential to ensure that data is clean, accurate, and secure, with clear responsibility assigned for maintaining this standard.
• 🤝 The concept of a 'service bureau' culture within the analytics organization is introduced, where the focus is on providing useful and utilized analytics to internal customers.
• 🌐 The script discusses the balance between centralizing and decentralizing analytics, suggesting that the first question should be about creating a service bureau culture rather than the structure itself.
• 🤔 It suggests that companies should consider the advantages of centralization, such as a culture that rewards and protects advanced data scientists, while ensuring their work is used effectively.
• 💡 The importance of having a framework to help companies decide how much to centralize or decentralize is mentioned, indicating that there is usually a mix of both approaches.
• 📍 The script identifies 'kingpin roles' such as data scientists, business solution architects, campaign experts, and advanced modelers as critical for leveraging big data effectively.
• 🛑 It concludes by emphasizing the need for these roles to ensure that models are not only created but also turned into actionable campaigns that impact consumers or customers.

Q & A

What are the traditional capabilities needed for building big data and analytics infrastructure?

The traditional capabilities include technology hardware and software, as well as the applications that people typically turn to.

Why is it important to build capabilities around organizational design, people, and processes in big data and analytics?

It is important because after building the infrastructure, organizations need to determine who will use the data, how it will be used, and what kind of analysts and roles are required for effective data utilization.

What roles are essential in ensuring that big data and analytics are used effectively within an organization?

Essential roles include data scientists, business solution architects, campaign experts, and advanced modelers, who are responsible for creating models, ensuring data accessibility, and turning models into actionable results.

Why is it crucial to have a service bureau culture within the analytics department?

A service bureau culture ensures that the analytics team focuses on providing useful and used analytics to internal customers, rather than just doing analytics for the sake of it.

What is the significance of data hygiene and data governance in big data analytics?

Data hygiene and governance are crucial because they ensure that the data used in analytics is clean, accurate, and secure, which directly impacts the quality of the results produced by the models.

What are some advantages of centralizing analytics within an organization?

Centralizing analytics can allow for a culture that rewards and protects advanced data scientists, ensuring that their work is used and useful within the organization.

What is the first question an organization should ask itself when considering centralizing or decentralizing analytics?

The first question should be how to ensure that the organization responsible for analytics views their role as a service bureau and provides useful analytics to internal customers.

What are the key roles that are critical for leveraging big data effectively and quickly?

The key roles include data scientists, business solution architects, campaign experts, and advanced modelers, who contribute to the creation, accessibility, and application of big data models.

How does the concept of 'garbage in, garbage out' apply to big data and analytics?

The concept applies as poor quality data input into a model will result in poor quality output, regardless of how sophisticated the model itself is.

What is the role of a business solution architect in big data and analytics?

A business solution architect is responsible for understanding and creating the right big data warehouse, ensuring that the data is accessible and useful in a usable way.

What is the importance of campaign experts in the context of big data and analytics?

Campaign experts are crucial as they ensure that advanced models are turned into actionable campaigns that can effectively engage consumers or customers.

Outlines

00:00

🤖 Building Organizational Capabilities for Big Data Analytics

The paragraph discusses the multifaceted nature of establishing a robust big data analytics infrastructure. It emphasizes that beyond the traditional focus on technology hardware and software, there's a critical need to build organizational capabilities around design, people, and processes. The speaker highlights the importance of determining who will utilize the analytics, the kind of analysts required (data scientists or just analysts), and the necessity for a business solution architect or a simple database. Furthermore, the paragraph underscores the significance of data quality, data governance, and ensuring data hygiene to avoid the 'garbage in, garbage out' scenario. The speaker suggests that companies should focus on creating a service bureau culture within their analytics teams to provide useful and actionable insights to internal customers. The paragraph also touches on the debate between centralizing and decentralizing analytics, advocating for a balanced approach with a focus on the right service bureau culture.

Keywords

💡Technology Hardware and Software

Technology hardware refers to the physical components of a computer system or network, such as servers, routers, and storage devices. Software, on the other hand, encompasses the programs and applications that run on these hardware platforms. In the context of the video, these are foundational elements that enable the building of infrastructure necessary for big data and analytics. The script mentions that while these are traditional aspects people turn to, there's more to consider, indicating that technology alone is not sufficient for effective data utilization.

💡Applications

In the script, applications refer to software programs designed for specific tasks or purposes, which can be used to analyze and interpret data. Applications are crucial in the big data context as they provide the means to interact with data, derive insights, and make decisions based on analytics. They are part of the broader ecosystem that supports data-driven decision-making.

💡Organizational Design

Organizational design involves structuring how an organization operates, including its management hierarchy, division of labor, and processes. The script highlights the importance of building capabilities around organizational design to ensure that the infrastructure for big data and analytics is effectively utilized. It implies that the structure and design of an organization can significantly impact how data is managed and used.

💡Data Governance

Data governance is the oversight framework for managing data availability, usability, integrity, and security in an organization. The script emphasizes the importance of data governance in ensuring that data is clean, accurate, and responsibly managed. It is a critical component in the big data analytics process, as it helps maintain the quality and reliability of the data being analyzed.

💡Data Hygiene

Data hygiene refers to the practices and procedures that ensure the quality and integrity of data. The script mentions the old adage 'garbage in, garbage out,' which underscores the importance of data cleansing and maintaining data hygiene. Poor quality data can lead to inaccurate analytics and decisions, making data hygiene a vital aspect of the big data infrastructure.

💡Data Scientists

Data scientists are professionals skilled in analyzing and interpreting complex digital data to aid decision-making. The script discusses the need for data scientists in the context of creating advanced models and ensuring that these models are programmatically repeatable. They play a key role in the big data analytics process by providing the technical expertise required to derive meaningful insights from data.

💡Business Solution Architects

A business solution architect is a professional who designs and implements technology solutions to meet business needs. In the script, they are mentioned as being responsible for creating the right big data warehouse, ensuring that data is accessible and useful. They are a critical link between the technical capabilities of big data infrastructure and the business objectives of an organization.

💡Campaign Experts

Campaign experts are professionals who specialize in planning and executing marketing campaigns. The script highlights their role as the 'last mile' in the big data process, ensuring that models developed by data scientists are turned into actionable campaigns that engage consumers or customers. They are essential in translating data insights into tangible business outcomes.

💡Advanced Modelers

Advanced modelers are individuals who specialize in creating complex predictive models using data. The script refers to them as being critical in the big data analytics process, as they develop sophisticated models that can provide deep insights. They also ensure that these models are programmatically implemented, reducing the need for manual intervention.

💡Centers of Excellence

A center of excellence (CoE) is a knowledge hub or a team of experts that provides thought leadership and best practices within a specific domain. The script discusses the idea of creating centers of excellence, both centralized and distributed, to focus on the breadth of capabilities needed for big data analytics. These centers are intended to foster expertise and drive innovation in data analytics.

💡Service Bureau Culture

A service bureau culture refers to an organizational mindset where the analytics team views their role as providing valuable services to internal customers. The script suggests that companies should focus on creating a service bureau culture where analytics are not done for their own sake but to help the business. This approach ensures that the analytics are useful and utilized by the business, driving better decision-making and outcomes.

Highlights

The importance of building capabilities around organizational design, people, and processes in addition to technology hardware and software.

The necessity of determining who will use big data and analytics and how it will be utilized within an organization.

The role of analysts and data scientists in an organization's big data and analytics strategy.

The need for a business solution architect to understand and create the right big data warehouse.

The significance of data governance in ensuring clean, accurate data for analytics.

The concept of 'garbage in, garbage out' and its relevance to data quality and model outcomes.

The importance of data hygiene and cleansing for effective big data analytics.

The debate between centralizing and decentralizing analytics within an organization.

The suggestion to first focus on creating a service bureau culture for analytics within the organization.

The advantages of centralizing analytics, such as fostering a culture that rewards and protects data scientists.

The critical roles of data scientists, business solution architects, campaign experts, and advanced modelers in leveraging big data.

The role of campaign experts in turning models into actionable campaigns that impact consumers or customers.

The framework for companies to determine the right balance between centralization and decentralization of analytics.

The idea that the first question should be about ensuring the analytics team views their role as providing useful analytics to internal customers.

The emphasis on the importance of the business solution architect in making data accessible and useful.

The discussion on the mix between centralization and decentralization, suggesting there is no one-size-fits-all approach.

Transcripts

the capabilities needed include the

traditional things that people run to

like technology hardware and software

and what kind of applications but the

truth is there's a lot of capabilities

that have to be built around the

organizational design people and

processes because the truth is when you

finish building your infrastructure and

you've got big data and big analytics

you've got to figure out who's going to

use it how's it going to be used

what kind of analysts are you're going

to have do you need data scientists or

just analysts you need a business

solution architect or is it a simple

database in addition to that how do you

make sure that the data you get is good

clean data you know the old garbage in

garbage out still applies garbage in to

a great model the model itself doesn't

give great results so data hygiene data

cleansing making sure data is clean and

having data governance around who is

responsible for keeping and securing

clean accurate data that gets fed into

the big data analytics becomes very very

important so it's more than just the

hardware infrastructure software and

applications it's the people and the

governance around it and we see

companies and clients working towards

centers of excellence and distributed

centers of excellence but they focus on

that kind of breadth a lot of our

clients start there should we centralize

our analytics so should we decentralize

them I don't think that's perhaps the

first question I think the first

question is how do you make sure that

the organization responsible for the

analytics looks at their job as a

Service Bureau and makes sure that they

are providing useful and used analytics

to internal customers there are

advantages to centralizing as an example

some of our more advanced data

scientists you know they're their

definition of a funny joke is about SAS

and sequel most of us don't get that

they want to be in a culture that makes

them rewarded and protected but they

want to see their analytics used and

useful and so yes you can focus on

centralized and decentralized but first

we would suggest focus on are you

creating the right service bureau

culture are your analysts doing

analytics for analytics sake or to help

the business and who in the business and

does that business user believe they

were

helped so that's a framework that we

find is very helpful as companies sort

through how much decentralize and how

much to decentralize and it's always a

mix between both there usually are four

or five kingpin roles where a tremendous

amount of deep expertise can be shared

and in a way that helps a lot of

internal constituents and customers

those roles really fall into the data

scientist category business solution

architects campaign experts and advanced

modelers those roles are really critical

the business solution architect is

someone who's going to really understand

how to create the right big data

warehouse so you can use the data and so

that it's accessible and useful in a

usable way the data scientists our folks

that are going to really help create

that advanced modeling but also they're

going to be able to programmatically

take those models and make sure they're

repeatable and use use programming

language to reduce some of the human

interaction the campaign experts are the

folks that that's that last mile if you

got a great model but you can't turn the

model into a campaign that touches a

consumer or a customer you've got

nothing so campaign experts are that

last mile that make sure that the models

get turned into results that turned into

actions and those are some of the key

roles that are really important to make

sure that a client can leverage big data

effectively and quickly