I originally published “Big Data Storymap” in January 2013 to introduce companies to the essential factors for a successful transition to big data and data science. Among other things, the tool enabled a creative representation of the data recovery process and was based on the maturity index of the big data business model.
1 – Big Data Storymap
It explains organizations the transition from their current state in business intelligence and data warehouse her future statethanks to big data, data science and value engineering (Figure 1).
The interest of such a narrative map was to provide my interlocutors with a visual representation using metaphors and landmarks to make them aware of the basics of a strategy. big data successful (i.e. data storytelling!)
Here are the main points of this map (see Figure 2):
- Milestone #1: Changing Market Dynamics Highlights market-related challenges that require a different approach to integrating big data (data and analytics) into an organization’s business.
- Milestone #2: Operational and IT Challenges Highlights the top challenges organizations face in transforming their business intelligence and data warehouse environments to realize the business benefits of big data.
- Milestone #3: Transforming Business Models for Big Data Provides a repository for companies to see how effectively they are using data and analytics to evolve their business models.
- Milestone #4: Road to Big Data Implementation Defines a process that drives alignment between IT and business to achieve actionable and business relevant outcomes.
- Milestone #5: Operationalizing Big Data Defines a data science process that can encourage the development and continuous improvement of data and items analytically to make the company’s big data capabilities operational.
- Benchmark #6: Value Creation Contains examples of business functions that could benefit from this path.
Figure 2: Landmark Big Data concept map
2 – The Big Data Game
In October 2018, I introduced a sort of extension to the Big Data Storymap that provides a fun – board game-like – experience to help organizations succeed in their journey.
I named it the game Big Data (Big Data Game Board).
Figure 3: The big data game board
The big data game shown in Figure 3 above highlighted the pitfalls companies could encounter when turning their data into value:
- Pitfall #1 : Were you “caught in the analytics abyss” or did you manage to “harness the economic value of data”?
- Pitfall #2 : Did you “get bogged down in the data swamp” or did you manage to “build a collaborative platform to create value”?
- Pitfall #3 : Are you in an “endless race for technology” or are you “focused on business outcomes”?
- Trap #4 : Are you “driving on overambitious promises” or have you chosen a “pragmatic approach to value engineering”?
- Trap #5 : Have you succumbed to the “level of limiting career decisions” or have you managed to climb the mountain of “business support design”?
Many practical tips as well as templates and tools related to this game help companies to create value through big data and data science.
3 – Map of the valorization journey of data management
These two tools aim to help stimulate creativity in organizations to encourage them to use their data and analytical elements more effectively to further develop their business models.
Now let’s move on to the third part of this visual, which lays the foundations of the data management journey by proposing a clear path to their appreciation. The “Data Management Value Journey Map”.
This map provides a clear roadmap from data to value. It integrates the disciplines of data management, data science and business management (see Figure 4).
Figure 4: Data Management Value Journey Map
This journey can be riddled with pitfalls and lead to wrong decisions that end in failure (☠️). In addition, neglecting or abbreviating certain steps can lead to a return to the beginning, resulting in a waste of time, financial and human resources and, most importantly, eroding the trust of those involved.
The purpose of this card is to highlight the need for close cooperation between all parties. This enables management to clearly articulate and quantify the business and operational value realized from implementing data and analytics across the organization (Figure 5).
Figure 5: The different disciplines
The Data Valorization Journey Map includes the following disciplines:
- value engineering breaks down an organization’s strategic initiative into its underlying requirements in terms of activities (stakeholders, use cases, key performance indicators), data and analytical elements.
- That data management collects, stores, organizes, labels, catalogues, manages and secures the data created and collected by an organization.
- data technology is responsible for the aggregation, preparation, normalization, grouping, ordering and processing of raw data. It’s also tasked with harnessing them downstream, within an organization, and at the enterprise ecosystem level.
- the feature engineering Leads the collaboration between domain experts and the data science team to mathematically select and transform data variables to develop parameters used to generate data models machine learning and AI.
- That Data Science create all models by AI/ML used to identify and codify the propensities, trends, iterations, and relationships buried in the data that can be drawn from it in terms of customers, products, and operations.
- ML engineering applies software engineering principles to data management and data science to manage, control and operationalize ML models in the company.
- the MLOps is a set of practices for reliably and efficiently deploying and maintaining machine learning models in production.
Summary: Create a culture that encourages the evaluation of data
These three tools aim to present everything companies need to unlock value from their data in a fun and usable way.
We hope they unleash the creativity and potential of everyone in your organization and enable you to better harness the power of big data and analytics.