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4 Steps to Accelerate Data to Insight Process

Updated: May 25, 2020

Data is growing exponentially (175 ZB by 2025 according to IDC) so does the need for insights, as John Naisbitt quotes “We are drowning in information and starving for knowledge”. Based on research from McKinsey, data is meaningless unless it helps make decisions that have measurable impact. However, most organizations are struggling with turning their existing data into actions (only 29% are successful according to this article on Forbes). The ultimate goal of any analytic initiative is to generate actionable insight, so at Integra we focus on accelerating this process for our clients.

Data grows exponentially but insight is lagging behind

With the rise of computing power and emergence of machine learning (ML) and artificial intelligence (AI), you can turn this vast amount of data into actionable insights. But can you do this fast an in a repeatable fashion?

Here is an approach you could adopt to generate insight and a few tips to make that process fast:

1. Ideation: Decide what your goals are and clarify the outcomes

First things first, you need to have a clear understanding of what problem you are trying to solve. Goals should be reachable and address the largest economic opportunities that fit your organization’s infrastructure, data, people and process capacity. Some familiar use cases in oil and gas industry are: predictive maintenance to prevent unexpected equipment failure i.e. pumps, manage and optimize energy consumption, optimizing drilling operation, etc.

Integra acceleration tips: To accelerate this stage, get organized, time box your activities and accept that failing at this stage is not a bad outcome (e.g. you might have identified an idea that is not feasible)

2. Exploration: Source your data, generate insights fast

Gather the data that is required to solve the problem. Try not to dive into a data lake, pick whatever data set that is available immediately and gives you the information you need. Once you have your insight, you can build on it with more data for longer term decision making. For example, you can start with analyzing basic time series data (e.g. SCADA) for one pump to recognize the failure patterns. As your insight network grows you can analyze more similar pumps and add more contextual data (e.g. maintenance history, operator logs, etc.) to gain a wholistic view of your pump performance.

Integra acceleration tips: Use existing technologies, don’t waste time and resources on building complicated technical solutions or tackle too much data. Breakdown the solutions into smaller pieces (e.g. start with an asset, then bring other assets, then bring additional factors into the exploration stage).

3. Validation: Validate your insights with subject matter experts

You need to validate your data discoveries through analyzing your model(s) metrics and decide whether they should be used in a real-world scenario. Analytic and data initiatives must yield results almost immediately and continuously in order to create the necessary

momentum and support across the organizations. A “need for speed” approach helps you move to action quickly with imperfect information.

Integra acceleration tips: Have frequent touch points with the SME’s, keep your models simple so it can easily adopt, make sure SME’s have time for you (prepare them before entering this stage).

4. Implementation: Implement the solution using agile methodologies

Once you have generated your insights, it’s time to turn them into actions. At the beginning, you are only targeting the front line of business and operation, who have the direct impact on the case. Through an agile process you develop a business case and create a road map for implementing insight-based solutions and adopting strategies based on your business needs and capacity.

Integra acceleration tips: Avoid following waterfall methodologies, use iterative approach and deliver value incrementally in a staged fashion. Use Agile methodologies to continuously engage the end users and deliver the solution following proper change management techniques in data and analytics.

Thanks to AI and ML technologies, it’s easier than ever to make data driven decisions which are cost, time, and labour effective and have a huge impact on growing and improving your business and operations. You can make your data work harder for you with a smart approach and right tools.

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