Understanding the Latest Big Data Best Practices That Engineers Love
Big Data is more accessible than ever. 31% of companies identify as “data-driven.” Data-driven practices improve outcomes across the board.
Yet, with Big Data comes big problems. Too many businesses are overwhelmed by their data. And, if data is inaccurate, it’s useless.
The typical company fails to use up to 73% of the data it acquires. As a result, many organizations are leaving the data-driven ethos behind.
This isn’t necessary. Fortunately, any company can make data work for them. Just implement Big Data best practices.
Here, we’ll explore the five Big Data business practices engineers love. Put all five in place, and you’ll set your company up for long-term success.
Table of Contents
At its best, Big Data can optimize every business decision. A company can integrate its analyses seamlessly into the workflow.
But, that doesn’t happen by accident. The most critical best practice is to develop a big data strategy. A company can do that in four steps.
Big Data can drive a range of strategic choices. It might support:
Choose which processes you want to improve with Big Data. Collaborate across teams to establish specific goals. Agree on metrics to measure how you’re meeting those goals.
There are a variety of data sources and structures. Data itself may be in a wide range of file types. Data sets can be structured, unstructured, or semi-structured.
Research the uses and characteristics of each data structure. Which structure best enables you to use data as a means to facilitate your goals?
Then, evaluate your data sources. Each piece of data set has four characteristics: variety, velocity (dynamism), volume, and veracity.
Weighing these characteristics lets you determine the final v: value. A domain expert can help rate a data set’s veracity.
Source high-quality data that you can easily use towards your objectives.
A company can’t improve every process at once. Start small. Meet with all departments to outline Big Data use cases.
Make sure each use case meets the business objectives. Be open to data revealing hidden patterns and correlations.
Then, prioritize. Plan to tackle processes with the biggest projected business impact first. Keep the budget in mind, and keep strategizing transparent.
Fill in the strategy with increasingly granular steps. Let data inform each step.
Remember, a strategy can be revised. Fill in steps with more granularity as information becomes available.
Dynamic data can overwhelm analysis efforts. Thus, too many companies neglect it.
Dynamic data sets are periodically updated. As new information becomes available, the system automatically updates the set. Points of dynamic data can include:
To prevent overwhelm, use real-time analysis thoughtfully. Ask, “could right-time analysis inform this business process just as well?” Weigh the merits of each.
Real-time analytics informs decisions as transactions happen. Many analytics tools are “decision support” tools. For example, investors use real-time analysis to navigate rapid market fluctuations.
But, real-time analytics isn’t the right choice for every business. Nor should it fuel every business decision. In some cases, right-time analytics is a wiser choice.
Right-time analytics requires an organization to manage incoming dynamic data. But, it doesn’t analyze the information until it’s the right time.
For instance, dynamic data can inform choices that optimize a company’s agility. Dynamic data also lets leaders detect operational problems early, and it can inform strategies for customer personalization.
Yet, a business doesn’t need to make these choices instantly. Instead, use right-time analysis to inform operational decisions.
Right-time analysis approaches dynamic Big Data in iterations. This approach lets you work with data when the time is right, without letting stored data degrade.
Real-time and right-time analyses are both useful. Either process might fit into your Big Data strategy at different stages.
Learn how different platforms prioritize different modes of analysis. Factor this into your strategy.
AI systems are increasingly powerful parts of the Big Data industry. So, how can you optimize AIs for different data-driven processes?
AI uses logic to make decisions.
An unsophisticated AI, like a chatbot, will follow a pre-programmed “if/then” flowchart. A complex flowchart lets it make nuanced decisions in response to input. But, it can’t learn new information.
Sophisticated AIs use machine learning (ML). This lets them learn from new information continually. New information prompts ML-driven AIs to update their decision-making processes.
Big Data encompasses dynamic data sets and the framework we use when parsing data sets. A machine-learning-driven AI analyzes data with an algorithm. Then, it outputs a model of the data.
For a machine to learn on its own, a smart engineer must train it. The three training methods are:
Training teaches the machine how to learn from new information. It encourages the machine to output useful, accurate models. Research each method to discover which one best suits your data.
First, cultivate an algorithm with the method that best suits your data framework. You might outsource this. Then, you can optimize your ML-driven AI program to meet your business’ Big Data needs.
To conduct a preliminary analysis, review initial hypotheses about the data, its source, its utility, and your algorithm. Examine:
If everything is in order, test the hypotheses. Then, run the algorithm.
Review any errors in the model. Are they classification errors? Noted errors guide future iterations of the algorithm.
To optimize your models and algorithms, conduct a baseline analysis. If the baseline isn’t useable, train a baseline model. Pre-trained models and cloud APIs are effective, time-saving options at this stage.
Center your business metrics as you optimize. Assemble multiple outputs with various algorithms. Choose algorithms with counterbalancing strengths.
Augment data with annotation. Annotation effectively expands training data sets. This lets you scale machine-learning-based analyses as the data volume increases.
Annotation can be a bottleneck. So, outsource the task to field specialists.
Augmentation encourages the machine’s active learning. Note outputs (models) that are confused or model incorrect predictions.
Catalogue relevant data sets. Then, send the sets and the resulting, erroneous output, to domain experts. They will annotate your data accurately and effectively.
Once specialists label the data correctly, incorporate the labeled data back into training. Supervised machine learning methods train effectively with labeled data.
This enhancement enables machines to better recognize desirable input-output functions. It improves the utility of its pattern recognition within parameters.
Supervised machine learning trains with set parameters. The annotated data itself informs those parameters.
This method cultivates algorithms for automated Big Data applications. Examples include:
You can also use augmented data to inform other training methods. Do some research, and learn how analysts use annotated data to inform reinforcement and unsupervised training.
Outputs can be hard for humans to decipher. Consider funneling models through a data manipulation tool.
Manipulating data makes it easier to read. Common manipulations include:
Manipulations streamline workflows by smoothing out transitions. At points, AIs hand off the data to humans.
Manipulations help us interpret the analyses correctly at a glance. Thus, we can move to the next stage swiftly.
Cloud computing technology now enables bulk-price data storage. Cloud platform vendors price data as a commodity, which lowers the cost. Many cloud data storage platforms also offer:
Outsourcing data storage to the experts is smart. It’s also a useful way to keep data safe and stay under budget.
Data governance is a set of data management practices. It empowers companies to comply with federal and international laws when they handle protected data. A complete data governance framework has four arms:
Optimization keeps data secure, useable, and available. You can optimize data governance along each arm of the framework. Consider these strategies.
Data governance documents should be adaptable. Like data itself, create a process to change documents as you get new information.
Policies should emphasize risk mitigation. Mandate oversight over different data assets by each asset’s value and vulnerability.
Make sure all rules comply with federal and international laws. Develop structures that make abiding by rules easy. As with policies, create opportunities to iterate rules in the future.
Data governance structures incorporate a manager, a team, and dedicated data stewards. The stewards are the first line of defense. They oversee data sets and enforce policy compliance.
Data governance software automates data management. Premium tools continually improve cybersecurity strategies.
Data governance software may offer features that streamline workflow management, data cataloging, and process documentation.
When you implement Big Data best practices, your business can thrive. Want to learn more tips for success? Check out more strategies in our content library.
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