Confessions of a Data Engineer

Confession #1: We see data differently than you do.
(And we know you’re spending too much on data.)

We know that we see data differently than most people we come across at work and in life. We also know that the way we view data makes data work easier and delivers more value. Our methodology improves data ecosystem capacity while driving down costs.

You can download our paper – 10 Ways to Save on your Data Spend, here. 

Confession #2: We don’t think your organisation needs a data strategy.

Instead, your organisation needs effective data operations that are aligned with your business strategy.  The better the alignment between data teams, tech teams and business teams, the more efficiently data and technology investment can be deployed to meet strategic objectives.

Confession #3: We know effective data ecosystems are more about people than they are about the technology.

A strong data culture is essential for a sustainable data ecosystem that successfully underpins business success. Without it, organisations experience increased security risks, hidden enterprise data costs, more downtime, stakeholder friction, missed deadlines, reduced business outcomes and single points of failure.

See 10 Ways Organisations Spend Too Much on Data

Confession #4: No matter what your business does, we see it as a decision factory.

Decisions lie at the heart of every business. Every decision is pertinent. The speed of the decision is crucial. The people involved in the decision are vital. In fact, your decision factory comprises all your people and all the decisions they make each day. Effective decision making relies on increasing the number of people who can participate in the process. This means ensuring every team member who should be making decisions is empowered to make the best possible decisions.

Good decision-making factories excel in many facets of decision making. They correct poor decision-making processes quickly to avoid bigger problems. Most importantly, a business that seeks to improve its performance as a decision factory places a priority on establishing the organisational data culture required to support and sustain effective decision making.

The more efficiently decisions are made, the more efficient the factory becomes. To outcompete in the market, it is crucial that data acts as the fuel powering the factory, helping you make better decisions. We call this ‘business logic’ and it forms a key part of the BizCubed data engineering methodology and framework.

The best decision factories rely on operational analytics for better and more robust decision making. They use deep insights to plan. Decisions across the business are backed by data, informed by insights gained by leveraging consistent operations, data workflows and reliable, governed data sets.

Confession #5: We want everyone to understand what data engineers do.

Ask a data analyst what the most frustrating part of their job is, and many will say that they spend too much time obtaining, cleaning, de-duplicating, validating, and performing other actions with data before they even get to perform any analysis. What’s frustrating to them is costing the business in time, money and missed opportunities.

Fixing this problem begins with understanding the difference between data engineering, industrial data engineering, and data science / analytics. Then you can enable data team members to do the work they should be doing.

Let’s go back to the decision factory metaphor.

Data is the fuel powering decisions.

A data scientist is an analytics professional who is responsible for collecting, analysing and interpreting data to help drive decision-making in an organisation.

Data scientists typically mine data for information that can be used to predict customer behaviour, identify new revenue opportunities, detect fraudulent transactions, and meet other business needs. They gather and prepare relevant data, and use analytics tools to detect patterns, trends and relationships in data sets. They develop statistical and predictive models to run against the data sets; and create data visualisations, dashboards and reports to communicate their findings.

Find full definition here

So, what about data engineering? Data engineers focus on delivering the fuel – the data – that your business needs, at scale. We ensure the data ecosystem functions to make data available, in repeatable, sustainable, and automated ways.

Industrial data engineering is the engineering discipline of optimising the decision factory.

We optimise your decision factory’s data workflows to ensure:

• That report you need is ready when you need it, every time.
• That dashboard is fed with real-time insights based on consistently fresh data inputs
• That advanced predictive analytics algorithm your data team uses to provide insights to inform any number of critical decisions is consistently fed with trustworthy, governed data following your organisation’s protocols, checks and balances, SLAs and specific requirements.
• That the insights your business uses to delight its customers are available to be leveraged, consistently, by whoever needs them.
• That technical debt compromises don’t accrue unnecessary costs
• That a consistent, operational approach to security is embedded in your data ecosystem
• That you have a bird’s eye view across all data processes to be able to pinpoint issues, optimise operations and drive down costs.
• That your digital twin (aka your data ecosystem) is a strong reflection of your business.