Today’s world is running on oil. That’s not correct. It’s running on data. The world daily generates 2.5 quintillion bytes of data as a result of the exchange of numerous emails, text messages, videos.  From customer acquisition to end-of-year business review, data influences every process in every industry. Businesses across the globe make millions of dollars in investment to get the right resources (man and machine) in a bid to position themselves to make data-driven decisions. Companies in the United State are willing to pay an average salary of $120,931 for a data scientist whose job satisfaction is at 80%. An average salary of over $84,000 per year for data analysts in most U.S start-ups. Statistics show an estimated 11.5 million new data-science jobs by 2026 (source: U.S. Bureau of Labor Statistics)

In today’s data-driven world,  there are various data needs to meet in every organization. The needs are quite cumbersome hence it is imperative to identify them and assign them to the appropriate talent/team member.

Data Entry Clerk

Who are they? 

Data Entry is clerical work that entails taking charge of entering all the data into different computer databases using various processes like typing, voice recording. Data entry clerks usually compile, verify, sort and maintain records effectively. Most data entry work within a data team. This position can be considered as the first tier on the data team. While this role usually requires no special/technical skills, they usually play a pivotal role in a data team, especially in research-based projects.

Why do we need them?

They aid the data collection process especially if the process is not automated.
They build surveys for online and offline systems and follow through to ensure the information are entered correctly.

Who should hire a data entry clerk?

Small teams and organizations who want to embark on small research projects

Why do we need them?

The ultimate aim of a data analyst is to aid data understanding and decision making. Top business executives may not be able to commit time to have a deep dive into the data available within the business; data analysts do this with ease.

Who should hire a data analyst?

Early start-ups or small-scaled organizations need a data analyst.

Why do we need them?

Every data scientist is responsible for data wrangling, analysis and initiating business data recommendation (proactive measures). While data analyst spends more time building dashboards and comparing actual performance with preset targets, data scientist usually develop models to suggest various business strategies like “what-if scenarios” to the executives.

Who should hire a data scientist?

A fasting growing business that generates thousands of rows of data that need to carry out customer segmentation, in-depth insights to unravel new business opportunities or threats needs a data scientist.

Why do we need them?

The main responsibility of a data engineer is to make data accessible by building pipelines so that organizations can evaluate and optimize their performance from a data perspective.

Who should hire a data engineer?

An organisation that has multiple data sources with structured/unstructured datasets and different formats of data needs a data engineer. Any data warehouse with tables whose number of rows have exceeded one million rows would perform best with the services of a data engineer.

Figure 1: A Comparison of the different roles using certain attributes.

The demand for data solutions and the constant flux in titles in the space in the last decade has prompted organizations to seek help to meet their data needs. Companies must define their business needs, layout a data-road map in line with their business process, build a data-driven culture within the team and employ the right data professionals for the job. It is imperative to note that most professional wears different hats hence there is a chance you might have a “Merlin” in your team who ticks all the boxes.

Written By:

kpofure Enughwure

Read more articles about data science consulting:

• Three Eras of Intelligence
• Data Engineering and Data Architecture

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The world of information is vast and ever-growing. How we make sense of it and use it to make decisions has undergone a fascinating transformation. Today, we’ll explore the “Three Eras of Intelligence Framework,” a lens through which we can view the evolution of decision-making.

• Era 1: Report-Driven Decisions (The Age of Data) – This era relied heavily on historical data and reports, often leading to “gut feeling” approaches.
• Era 2: User-Driven Decisions (The Age of Interaction) – Here, user feedback and interaction became crucial. Businesses turned their focus to understanding customer needs and preferences.
• Era 3: AI-Driven Decisions (The Age of Intelligence) – This emerging era leverages the power of Artificial Intelligence for deeper analysis and more intelligent decision-making.

Throughout this exploration, we’ll delve into specific examples within different industries and discuss the strengths and weaknesses of each era. Get ready to discover how we’ve moved from basic data analysis to a future powered by AI-driven intelligence!

Here’s a breakdown of the “Three Eras of Intelligence Framework” focusing on decision-making:

Era 1: Report-Driven Decisions (The Age of Data)

• This initial stage where decisions rely heavily on historical data and reports. Think of it as the “gut feeling” informed by past experiences.
• Information is gathered through manual processes: manually entering data into spreadsheets, conducting in-person surveys, or compiling reports from physical documents. These methods often result in limited data sets due to the time-intensive nature of data collection and the potential for human error.
• The analysis involves basic tools like spreadsheets and rudimentary statistical techniques.
• Strengths: Straightforward, familiar approach. Easy to understand and implement.
• Weaknesses: Prone to bias based on past experiences. Limited capacity to handle complex problems or future predictions.

Era 2: User-Driven Decisions (The Age of Interaction)

• This era marks a shift towards user feedback and interaction.
• Data collection has become more sophisticated, incorporating customer surveys, social media sentiment analysis, and market research.
• User experience (UX) takes center stage, with decisions influenced by user needs and preferences.
• Strengths: More data-driven than Era 1, leading to more targeted and relevant decisions.
• Weaknesses: Can be time-consuming to gather user data. This may overlook broader trends or hidden patterns in the data.

Era 3: AI-Driven Decisions (The Age of Intelligence)

• This is the emerging era where Artificial Intelligence plays a pivotal role in decision-making.
• Massive datasets are analyzed using Machine Learning algorithms, uncovering hidden patterns and insights.
• AI can predict future trends, identify risks, and optimize decisions in real time.
• Strengths: Unprecedented level of data analysis, leading to faster, more accurate, and unbiased decisions.
• Weaknesses: Requires significant investment in AI infrastructure and expertise. The explainability of AI decisions can be challenging.

Tools of the Trade: Unveiling the tools used in each era of intelligence

In the ever-evolving realm of intelligence, the tools we use to learn and process information have transformed dramatically. We’ll explore the groundbreaking instruments that have shaped each stage of intelligence, from the rudimentary to the revolutionary. Prepare to delve into the fascinating history of how we’ve become smarter, tool by tool.

Era 1: Report-Driven Decisions (The Age of Data)

• Tools: Spreadsheets, basic statistical software (e.g., basic descriptive statistics), paper reports.

Era 2: User-Driven Decisions (The Age of Interaction)

• Tools: Surveys, customer relationship management (CRM) systems, social media monitoring platforms, basic sentiment analysis tools.

Era 3: AI-Driven Decisions (The Age of Intelligence)

• Tools: Machine Learning algorithms (e.g., decision trees, random forests, deep learning), Big Data analytics platforms, Natural Language Processing (NLP) tools for analyzing text data, and visualization tools to interpret complex data.

As we move through the eras, the tools become more sophisticated. We transition from simple data analysis with spreadsheets to harnessing the power of AI and Big Data for deeper insights. It’s imperative to note that these tools often overlap. Businesses might still utilize elements from earlier eras while adopting newer ones.

Examples of Industries in each era

Let’s zoom in and see how these eras played out across different industries. Imagine retail in Era 1 relying solely on sales records. Now, fast forward to Era 3, where AI personalizes product recommendations. Let’s explore how industries finance, manufacturing, and retail have adapted their decision-making muscles throughout these three eras.

Era 1: Report-Driven Decisions

• Retail: Inventory management relied on historical sales data to determine stock levels.
• Finance: Loan approvals were based on credit reports and financial statements.
• Manufacturing: Production schedules were based on past demand patterns.

Era 2: User-Driven Decisions

• Retail: Online reviews and loyalty programs provide insights into customer preferences, influencing product selection and marketing strategies.
• Finance: Banks analyze social media sentiment to gauge consumer confidence and adjust interest rates accordingly.
• Manufacturing: Customer feedback and focus groups help designers create products that meet evolving needs.

Era 3: AI-Driven Decisions

• Retail: AI algorithms analyze customer purchase history and recommend personalized products.
• Finance: Machine learning can identify fraudulent transactions and predict creditworthiness with great accuracy.
• Manufacturing: Predictive maintenance based on sensor data prevents equipment failures and optimizes production processes.

These are just a few examples, and the integration of AI is rapidly changing how decisions are made across industries. The key takeaway is the progression from relying solely on historical data to incorporating user feedback and ultimately leveraging AI for more intelligent and data-driven decision-making.

Conclusion

The ever-evolving landscape of intelligence presents a captivating story of human ingenuity. From the rudimentary tools of the first era to the dawn of artificial general intelligence, each advancement builds upon the last.

Ready to evolve your organization’s approach to intelligence? Contact our team of experts today! We can help you evaluate your current stage, explore the possibilities of the next era, and develop a customized strategy to unlock the full potential of these groundbreaking tools. Stay ahead of the curve and propel your organization towards the future of intelligence.

Read more articles about data science consulting:

• Data Entry Clerk, Data Scientist, Data Analyst, Data Engineer
• Data Engineering and Data Architecture

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“In the digital age, data is the new oil. But unlike oil, it’s a renewable resource that can be refined, shaped, and molded into invaluable insights. Data architecture and data engineering are the architects and engineers behind this transformation. They design, build, and maintain complex systems that extract, transform, and load data into a usable format.

Imagine a world without data: no personalized recommendations, no advanced medical research, no self-driving cars. Data architecture and data engineering are the unsung heroes that make this digital world possible. Let’s dive into the fascinating realm of data and explore how these critical fields are shaping our future.”

Data Architect vs Data Engineer

In civil engineering, an architect will provide the foundation of a building for his clients. Likewise, a data architect offers a system design of a big-data solution for his clients. This role is key since it is cheaper to carry out changes in the design phase compared to the implementation phase.

See data engineers as construction personnel who lay blocks on the construction site based on the architect’s plan. The data engineers are responsible for building, deploying, and testing data pipelines for big-data solutions.

For one to be an excellent data architect, he must have strong hands-on experience in data engineering since there is a need for data architects to have a strong grasp on system development.

Data Architecture

In this phase of the big data, the following processes will be implemented:

1.      Gather the project requirements

The most important thing for a data architect is to gather the project requirements and refine them. Questions like what is the business trying to do? There is a need to understand the business needs. These needs can be AI solutions, data warehouse, and/or visualization solutions. Data architects must evaluate the existing technical environment available at the client’s end (Cloud or on-premises database, tools/services and programming languages (Python, Java, C or others). Data architects must explore and evaluate the data available in the environment (volume, structure or unstructured, velocity, veracity). Data architects must predict the in-flux of data when the product/solution is in use. The Data Architect must discuss the pain points, challenges, risks, constraints, budget, and scope with the stakeholders. Finally, data architecture must document all the project requirements before signing off on the project.

2.      Define the High-Level Architecture.

In this phase, data architects must have a vision of how the solution will look like.  What kind of data platforms will be used in this project (databricks or others). The supporting services like storage plan, and the data transformation technique needed in this project (extract, transform and loading (ETL) or extract, load and transform (ELT). Get security involved especially when your solution is used in the banking and healthcare sectors.

Avoiding unnecessary risks

There are some ways data architects can avoid investing too many resources on a project while meeting the client’s requirements. They are:

a.  Proof of Concepts

Proof of concepts are mock-ups or simple scaled-down versions of the architecture as a “sanity check” that it will meet the requirements. This also involves test key requirements such as row-level security.

b.  Pilots & Minimum Viable Products (MVP)

Pick a subset of the solution functionality or business area to develop and deploy an initial phase of the solution. This limits the risks and helps identify problems earlier. It is worth noting that pilots are real deliverables.

Data Engineer

The primary responsibilities of a data engineer are to: develop, test and deploy data pipelines.

Develop Pipelines

This phase entails writing code to get data from sources, land it in storage, clean and transform it, and aggregate it and save it to the solution layer in a form that is usable.

Testing

Running test data through the pipeline and verifying the output matches the requirements.

Deploying Data Pipelines

Automate deployment via the appropriate tools (GitHub Actions, Azure DevOps, Scripts, Data bricks Asset Bundles).

Which is more important?

Data architecture is the foundation upon which data-driven initiatives are built. It provides a blueprint for how data will be collected, stored, processed, and analyzed. A well-designed data architecture ensures that data is accessible, reliable, and secure. It also facilitates data governance, ensuring that data is used ethically and responsibly. By laying a strong data foundation, organizations can make informed decisions, improve operational efficiency, and gain a competitive edge.

Errors in data architecture can be extremely costly to fix. The longer an error goes undetected, the more data can be corrupted or lost. This can lead to inaccurate analysis, flawed decision-making, and even regulatory violations. Additionally, rectifying errors in complex data architectures often involves significant time and resources, as it may require rebuilding entire data pipelines or migrating data to new systems.

The earlier in the process you make errors, the costlier:

Architecture: Errors at this stage can have the most significant impact, as they can fundamentally affect the entire data infrastructure. Fixing these errors often requires extensive re-engineering and potentially rebuilding system parts.

Design: While not as severe as architectural errors, design flaws can still be costly to address. They may involve rethinking data models, data flows, or system components.

Construction: Errors discovered during the construction or implementation phase are generally less expensive to rectify. They often involve specific coding issues or configuration problems that can be addressed more directly.

The key is to identify and correct errors as early as possible. Rigorous testing, code reviews, and quality assurance practices are essential to prevent costly mistakes. “Want to learn more about data engineering and architecture? Subscribe to our newsletter, share this post, or contact us for a free consultation. We can help you build a robust data infrastructure tailored to your specific needs.”

Read more articles about data science consulting:

• Three Eras of Intelligence
• Data Entry Clerk, Data Scientist, Data Analyst, Data Engineer

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