Have you ever heard of the benefits from running a Data Landscape?
The amount of data that companies generate is growing every day. In fact, in 2020 each person in the world generated 1.7MB of data every second, adding up to 2.5 quintillion bytes in a day! (see here). Companies are trying to keep up with this staggering pace, collecting more data than ever before (see here). Now there is a difference between collecting and leveraging. So, the question is, how can you effectively do that?
In the realm of pricing, businesses now have a huge opportunity to use big data and to develop advanced analytics to improve decisions and set optimal prices. In effect, pricing is the most effective lever to boost profits, just a 1% increase in price could generate an 8% growth in operating profits (see here). However, the process is painful and complex, with multiple issues such as data quality, outdated data architecture or organizational culture, among many others.
Why is a Data Landscape important?
The quality of data determines your ability to generate insights and make pricing decisions, but most importantly it has an inherent cost of management. In fact, according to IBM these costs represent $3.1 trillion every year for the US economy (see here). In order to mitigate these costs, during the Data Landscape process we identify issues and opportunities of improvement for data and architecture, using them as input to build a roadmap to provide guidance and to achieve the next level of data maturity.
When to do a Data Landscape
Companies pursuing the benefits of data driven pricing decisions use different levels of analytics: descriptive, predictive and prescriptive. However, these tools add little value without owning a scalable and sustainable pricing database with high data quality. Thus, Data Landscape is a must before making any investment on any tool that is fed with data.
How to run a Data Landscape
The process to create the Data Landscape is composed of 5 key activities:
- Map the required data fields
- Assess its data quality
- Identify opportunities of improvement
- Estimate financial potential impact
- Prepare the data to put into production for both descriptive and prescriptive levels of analytical and algorithmic tools
At TheTopLineLab, we execute this process with a team of Consultants, Data Scientists and BI Developers doing several iterations and actively working with the client. The purpose of it, is to be aligned with the customer both on the business application and the maturity expectations of the resulting data output.
What are the Key Success Factors?
Four key success factors are identified.
Engage the Data Team. The data team are the people who know best which the data fields are that the company owns, where they are located and which formulas or rules are applied. So, engagement of your data team is key in this process!
Willingness to migrate to better performing Data Architecture. The Data Architecture can be a constraint to industrialize the scalability and sustainability of the pricing database. Make sure that your architecture allows it!
Agile methodology. The Data Landscape steps are not linear, but iterative. Recurrent validation on assessments as well as on data preparation is required. Try to find alignment from expectation and to learn from previous loops!
Potential impact estimation. The Data quality issues need to be financially estimated to later prioritize them and to put them on the Roadmap based on the agreed objectives, the effort and the resources required. Remember: what cannot be measured cannot be managed!
So, how can we help you?
As part of our Data Preparation service we deliver what we call a Data Landscape, to help our customers to overcome these issues and build a scalable and sustainable database for pricing data driven decisions and analytics. We can help you in creating the Data Landscape if your company wants to invest in improving pricing analytics or any other data dependent tool. Don’t put the cart before the horse if not, you might be at risk to destroy your company value.
Do you want to know more? Please send an email to email@example.com