Agile Big Data and Many-Particle approach change Marketing and Sales effectiveness

Big data projects have broad impact on organizations. Big Data implementation overtakes
what normally Many-Particle data aggregation goodcould be considered a new way to conduct data management to business alignment. With Big Data the path from data sources to data intelligence changes drastically. The way to design and implement data intelligence definitively changed access, ingest, distil, processes and data visualization as well. Big data projects meet agile implementation, shorten the data intelligence lifecycle by increasing services capability and adequacy to fast-growing datasets, fast moving business. Accordingly, agile practice and many-particle approach minimize data entropy together with data access time cycles everywhere, preserve data security and enhance user experience to business instant realignment.

Contents
Introduction
Data Topology and Agile Big Data
The Many-Particle approach
Conclusion
Acknowledgment
References

Introduction
The way to move from today business data into Big Data intelligence could be a costly and time consuming process that could decrease the tremendous advantage of the Big Data and Cloud paradigms. Today, information is still misaligned with the business although the huge efforts of the past business intelligence projects: companies still use partial quantities of the real corporate data heritage. As a consequence, the data spectrum exploited is unpredictable and the process to align data and business is a long-term process. Agile Big Data aligns instantly data heritage and business data. Continuous data ingestion and distillation drastically reduces ETL process to run intelligence on the “big data-lake” when needed. Then, on-premise big data topology and functional data intelligence have a crucial role to meet profitability, customer affinity and fast moving business goals. This paper introduces the business case for Big Data to avoid Marketing and Sales data entropy, reduce risks and increase the likelihood of an aware and successful Big Data implementation.

Data Topology and Agile Big Data
Documenting data evolution and updating in the past could be considered a good practice in managing data. In the beginning of cloud paradigm, due to the cost cut down attraction, the practice to have a map of the company data heritage became a great benefit especially when services have to be subscribed in the cloud. Data models, a way to document the data heritage, evolved into MaaS (Model as a Service) that supports agile design and deliver of data services in the Cloud and makes the difference in planning a Big Data implementation project.

Considering data models doesn’t mean structured data only. On-premise models map data coming from structured, semi-structured and non-structured sources. Data models maps defined services topology would be moved on-premise or in the cloud. Still data models is needed for early exploration analysis and “ab-initio” services classifying parameters which define services boundaries (to personal cloud, financial parameters or healthcare positions, for example); data models (on SQL, No-SQL, Vectors or Graph structures) essentially doesn’t address the meaning the data have but identify the services’ classes before creating the data-lake. Of course, into the data-lake converge unusable data, unstructured or denormalized raw datasources as well. The more aware is the on-premise topology, the more secure and localizable is the big data usage both on-premise and in the Cloud. Further, agile MaaS approach reveals business process affected, operating requirements and stakeholders.

Big Data CorporateFig. 1 – Corporate Data-Lake and Agile Big Data approach

Accordingly, agile Big Data practice sets the link among on-premise data topologies and on-premise or in the cloud data intelligence. Topology leverages the company services asset into specific business objectives and will determine the successful user experience requirements and the proper rapid alignment with respect to the competitors.

This means that two crucial aspects have to be taken care:

  • Data is the “compass” to understand services capacity, stakeholders, culture of the organization: big data agility is based on data-driven approach. Therefore, in the incoming project setup minimize functional data behaviour. Use MaaS topology to define projects use cases data-driven. Data-driven project design defines data ingestion architecture and data landing into the data-lake and assist in understanding the best policy for continuous data feeding. Do not disregard this aspect: accurate data feeding is the core of Big Data approaches;
  • Move data analysis and functional aggregation to data intelligence applied on the data-lake. During ingestion and data landing data treatments have to be minimized Agile Big Data approach considers 2 zones: the in-memory one, based on data topology and on-premise supported by MaaS and data intelligence based on functional analysis and programming working on spare data.

Still, minimize any approach based on “ab-inizio” technology and software development. The Big Data ecosystem provides excellent platforms and MaaS agile approach helps to shift later the final technology choice/selection. Further, MaaS agile practice assists to clarify successes and failures zone and set expectations by time. This happens why when services have been set by on-premise topology then a link has been stretched among the data heritage and the data intelligence. There are no constraints between the raw data (documented or not) and the user experience that will leverage functional and business alignment. In the middle, only the data-lake exists, continuously changing and growing, continuously supplying information for the data intelligence ending.

The Many-Particle approach
Today, more of 70 percent of the world’s information is unstructured, not classified and, above all, misused: we are assisting to the greatest Marketing and Sales data myopia since they exist. Still, there is no awareness of the Big Data benefits for service and/or product companies, and again how the product’s companies can change their services based on goods production: great amount of data, exceptionally growing, high entropy, unknown correlations and limited data usage. The concept of on-premise topology introduces services as data-driven aggregation states applied to given parts of the data-lake. But this is what happens to many-particle system instability (yottabyte is 1024 byte with a binary usage of 280). Big data storages dimension near data-lake to many-particle systems. This vision destroys any traditional approach to Marketing and Sales.

If we consider the big data-lake, it contains fast moving content in order of data affinity and mass correlation. Depending upon dynamic data aggregation, data topologies may change by tuning on-premise data mapping. Consider data-lakes are mainly fed through:

– ingestion, distillation and landing from content based (datasources, datasets, operational and transactional DB’s);
– ingestion and distillation from collaborative feeding (dynamic collections of large amount of information on users’ behaviours coming from the internet, direct and/or indirect).

Collaborative ingestion can be managed as a content based as well in case of time needed to data intelligence ending has no strict constraints so to define a third method, the hybrid one.

This brief introduction tries to explain that the data-lake maps ab-initio topologies to services but also may classify more ecosystems the services are defined and applied to. Services live in the ecosystems and ecosystems depend upon data aggregation (why used, where used, how used, who uses) and just like aggregation states, big data density change dynamically. These changes are a consequence of datasources ingested, users experiences, customers behaviours, ecosystems interaction and, of course, business realignment. Marketing and Sales should change accordingly. But since data-lake may grow by 40 percent per year (in line with the estimation of the worldwide rate of information growth taking into account that unstructured data is growing 15 times faster than structured data – source IBM®), there is no way to get any (predictive) control for marketing and sales organization although data warehousing and/or sophisticated traditional data mining and analysis are in place.

Anyway, the data growth will be greater than ever in the next years and so the variance for data aggregation in the data-lake will have an exponential rising: this means many opportunities could be lost and again further marketing and sales entropy. Ab-initio topology by agile big data approach and functional programming applied to the data-lake supply the best answer for prescriptive analysis on many-particle big data systems. In fact, the data-lake allows to work on data cross-aggregation optimization, customer experience and aggregation states for services realignment with respect to the business ecosystems. Still, data-lake is an extraordinary real-time “what-if set” for prescriptive scenarios, data processing assumption and data risk propensity.

Data-Sea

Fig.2 – The Data-Lake is quickly becoming a Data-Sea with multi-particle-like data behaviour and dimension

Banking and Goods Production are 2 typical examples of Big Data agile implementation. Both are supplying services. Both are trying to align instantly and proactively offer and business changes. Banking and Financial services play a strategic role in relationship management, profitability performance to corporate groups, client companies and commercial banking networks. This is why financial applications need to be rapidly synchronized to ecosystems fluctuations states as ecosystem participants’ change everywhere their behaviour due to local and international business conditions. Functional big data paradigm working on many-particle data aggregation is prescriptive with respect to unpredictable services transition: it agilely realigns ecosystem services directions over on-premise data topologies mapping.

Goods production may tune services as a consequence of user’s experience by, for example, executing more focused and less time-consuming recommender systems. Goods production companies are in the run to provide personalized technical and commercial services, greater client loyalty and prescriptive offers starting soon when the clients interact or navigate the company website. With agile big data and many-particle approach, goods production potentially increases user similarity by data-lake massive data aggregations. Fast moving data aggregations constantly feed functional data intelligence to services realignment and topological correlations repositioning on-premise data similarities.

Two different paces, the same objective: be prescriptive, understanding “at earlier” which data aggregation state is the most proper along the data-lake instability and then contiguously realign products offer, services configuration and, consequently, keep ecosystems oversee: on-premise topology gauged on data-lake volume, data velocity and variety allows Marketing and Sales to tune on effective data aggregation to promptly adjust services to the ecosystem.

Conclusion
Client sentiment and user experience behaviour analytics allow rapid changes to product offerings or customer support which in turn enhance customer fidelity and business improvement. However data are growing exponentially and business alignment have to be provided in more decentralized environments. Agile MaaS approach based on data-driven raw volume, data velocity and variety together with on-premise services topology is a relatively low cost and light model. Topology does not influence data treatment. Data remains intact although services integrity and classification drive business, user experience and ecosystems alignment. Accordingly, agile practice and many particle approach we introduced minimize data entropy together with data access time cycles everywhere, preserve data security and enhance user experience to functional visualization realignment.

Acknowledgment
I sincerely thank Paolo La Torre for his precious feedback on contents and encouragement on publishing this paper. Paolo is working as Commercial, Technical and Compliance Project Supervisor for Big Data planning and engagement directions in finance and banking.

References
N. Piscopo, M. Cesino – Gain a strategic control point to your competitive advantage – https://www.youtube.com/watch?v=wSPKQJjIUwI
N. Piscopo – ID Consent: applying the IDaaS Maturity Framework to design and deploy interactive BYOID (Bring-Your-Own-ID) with Use Case
N. Piscopo – A high-level IDaaS metric: if and when moving ID in the Cloud
N. Piscopo – IDaaS – Verifying the ID ecosystem operational posture
N. Piscopo – MaaS (Model as a Service) is the emerging solution to design, map, integrate and publish Open Data
N. Piscopo – Best Practices for Moving to the Cloud using Data Models in the DaaS Life Cycle
N. Piscopo – Applying MaaS to DaaS (Database as a Service ) Contracts. An introduction to the Practice
N. Piscopo – MaaS applied to Healthcare – Use Case Practice
N. Piscopo – ERwin® in the Cloud: How Data Modeling Supports Database as a Service (DaaS) Implementations
N. Piscopo – CA ERwin® Data Modeler’s Role in the Relational Cloud
N. Piscopo – Using CA ERwin® Data Modeler and Microsoft SQL Azure to Move Data to the Cloud within the DaaS Life Cycle

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