Category: Analytics

DXP Series, Part IV: DXP and Data-Driven Decision-Making

Posted by on December 06, 2017

Business team meeting analysis financial chart together at cafe.

Think about how you make important business decisions. Decision-making begins at the point where intuition takes over from analyzing the data.  If your data analysis carries far less weight than intuition, your decision process may not be taking full advantage of available data.

If so, you are not alone. surveyed over 720 businesses. The survey found that 58 percent of respondents based about half of their regular business decisions “on gut feel or experience.” On the other hand, over 67 percent of those businesses “highly valued” information for decision-making, and 61 percent considered information “as an asset.”

The survey showed that when businesses were not using information as the basis for decision-making, it was because the information was not available or reliable. They were either not collecting it or were not using what they had.

KPIs are there, but not the data to read them

Another significant finding involved the role of key performance indicators. There is an important connection between KPIs and the data that measure and drive them. Here is where another disconnect stood out like a beacon: Nearly 80 percent of the companies had defined and standard sets of KPIs, but only 36 percent were using them “pervasively across the organization.”

So there was an obvious disconnect between valuing the information and a willingness to use it. In this post we shall address that contradiction and explore ways to close the gap between valuing the data and using it for data-driven decisions.

How DXP leverages data analytics

The road to data-driven decisions must go through data analytics.  In a previous blog, we discussed how data analytics and other tools plug into the realm of DXP. Data analytics are what help you find meaning in the data you generate and collect.

Those meanings are what drive the decisions and strategies that focus on efficiency and excellent customer service. In terms of business decisions, the ones based on verifiable and quality data are the most beneficial to the business. They are data-driven.

So, data-driven decision management is a way to gain advantage over competitors. One MIT study found that companies who stressed data-based decisions achieved productivity and profit increases of 4% and 6%, respectively.

Two “how-tos” to get on the road to data-driven decision management

#1. How to head towards a data-driven business culture (and benefit from it)

The survey showed that respondents were operating at half capacity when it came to using data-driven decision methods. To unlock the process as well as the data, businesses need to do the following:

  • Focus on and improve data quality.
  • Ease and lower the cost of information access. Break down those proprietary silos and use the best data-extraction tools available.
  • Improve the way the organization presents its information. There are many outstanding presentation products on the market.
  • Make the information easier to find, and speed up the process where users can access the information.
  • Get senior management on board and aware of the value of business intelligence and data-based decision making. Promote a culture of collaborative decision-making.

#2. How to improve internal data management

Data governance (where the data comes from, who collects and controls it) is a major obstacle to taking advantage of data-driven decision benefits. Survey recommendations were that companies should take the following steps:

1. Build an IT architecture that is agile and which can integrate the growing number of data sources required for decision-making. Plug into external big-data sources and start harvesting them.

2. Look for ways to break down barriers to promote cross-departmental cooperation and data alignment. A business intelligence competency center (BICC) can play a major role in achieving that goal.

3. Re-define and use KPIs across the organization and align those measures of success with a focus on data governance.

A strategy for applying data-based decision-making

Bernard Marr in his Forbes online piece, provides the following suggestions for any business to for applying data to decision making:

1. Start simply.

To overcome the overload of big data and its endless possibilities, design a simplified strategy. Cut to what your business is looking to achieve.  Rather than starting with the data you need, start with what your business goals are.

2. Focus on the important.

Concentrate on the business areas that are most important to achieving the foregoing strategy. “For most businesses,” says Marr, “the customer, finance, and operations areas are key ones to look at.”

3. Identify the unanswered questions.

Determine which questions you need to answer to achieve the above focus. Marr points out that when you move from “collect everything just in case” to “collect and measure x and y to answer question z,” you can massively reduce your cost and stress levels.

4. Zero in on the data that is best for you.

Find the ideal data for you: the data that will answer the most important questions and fulfill your strategic objectives. Marr stresses that no type of data is more valuable or inherently better than any other type.

5. Take a look at the data you already have.

Your internal data is everything your business currently has or can access. You are probably sitting on much of the information you know you need. If the data has not been collected, put a data collection system in place or go for external resources.

6. Make sure the costs and effort are justified.

Marr suggests treating data like any other business investment. To justify the cost and effort, you need to demonstrate that the data has value to your long-term business strategy. It is crucial to focus only on the data you need. If the costs outweigh the benefits, look for alternative data sources.

7. Set up the processes and put the people in place to gather and collect the data.

You may be subscribing to or buying access to a data set that is ready to analyze, in which case your data collection efforts are easier. However, most data projects require some data collection to get them moving.

8. Analyze the data to get meaningful and useful business insights.

To extract those insights, you need to plug into the analytics platforms that show you something new. Look for platforms that squeeze out the reports, analysis, and switchboard displays that tell you what you need to know.

9. Show your insights to the right people at the right time.

Do your data presentation in a way that overcomes the size and sophistication of the data set. The insights you present must inform decision-making and improve business performance. Go for style, and substance will take care of itself.

10. Incorporate what you learned from the data into the business.

Here is where you turn data into action. When you apply the insights to decision making, you transform your business for the better. That is the crux if data-driven decision-making. It is also the most rewarding part of the venture.

Summary and Conclusions

1. Business decision-making based on data results in greater reliability, efficiency, and profitability. DXP leverages data analytics towards the goal of more data-based decision making and achieving a competitive advantage.

2. Migrating towards a data-driven business culture requires unlocking the 50 percent of the decision-making and data currently not being used. It requires improved internal data management and governance and breaking down barriers to internal communication.

3. Finally, when those barriers are down, you can begin a strategy for applying data-based decision making. Start simple and focus on what business areas you need to improve and determine what data you need. No data is better or more valuable than any other; the key is to find the data that meets your objective, analyze it, and translate it into actionable decisions and improvement.

Find Meaning in Your Data With Elasticsearch

Posted by on March 28, 2017

We’re surrounded by data everywhere we go, and the amount is growing with each action we take. We rely on it regularly, probably a lot more than we even realize or would like to admit. From searching for nearby restaurants while traveling, to reading online product reviews prior to making a purchase, and finding the best route home based on real-time traffic patterns, data helps us make informed decisions every day.

However, all that data on its own is just data. The real value comes when you can find the right, relevant data when it’s needed. Better yet, take it a step further and find meaning in your data, and that’s where the real goldmine is.

Businesses are increasingly turning to search and analytics solutions to derive more value from their data, helping to provide the deep insights necessary to make better business decisions. Some popular use cases include:

  • Intelligent Search Experiences to discover and deliver relevant content
  • Security Analytics to better understand your infrastructure’s security
  • Data Visualization to present your data in a meaningful way
  • Log Analytics to gain deeper operational insight

At Rivet Logic, we realize the importance of data, and see the challenges businesses are facing in trying to make sense of their growing data pools. We’re excited to have partnered with Elastic – the company behind a suite of popular open source projects including ElasticsearchKibanaBeats, and Logstash – to deliver intelligent search and analytics solutions to help our customers get the most value out of their data, allowing them to make actionable improvements to websites for enhanced customer experiences!

A Real-world Use Case

How might this apply in a real-world scenario, you ask?

An example is a global hospitality customer of ours, who has partnered with Rivet Logic to implement three internal facing web properties that enable the company to perform its day to day business operations. With a reach spanning across 110+ countries, these sites are deployed in the cloud on Amazon AWS throughout the US, Europe and Asia Pacific, consisting of many data sources and used across multiple devices.

This customer needed a way to gain deeper insight into these systems — how the sites are being used along with the types of issues encountered to help improve operational efficiencies. Using Elasticsearch and Kibana, this customer is able to gain much better visibility into each site’s utility. Through detailed metrics, combined with the ability to perform aggregations and more intelligent queries, this customer can now gain much deeper insight into their data set through in depth reports and dashboards. In addition, the Elastic Stack solution aggregates all system logs into one place, making it possible to perform complex analysis to provide insightful data to better address operational concerns.