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Smartphones, sensors, chips… Digitalisation marches on unchecked. And with it comes an explosion in the quantities of data. But how can we sensibly use this?

These new data sources from digitalisation also harbour great potential. For example, now we can analyse when, where and perhaps even why customers change their purchasing behaviour. It simply makes sense to use this information. This fundamental idea is the basis of Big Data applications, as they are known in the industry, that make a preliminary analysis of data so as to make active use of predictions about the behaviour of people and machines.

Business Intelligence and Big Data therefore follow the same fundamental principle of data allocation, data storage and analysis; they just use different methods and technologies. And which analysts want to know whether their base data come from structured or unstructured data pools? It is time for new analytical platforms that no longer separate these. It is time for Analytics 3.0.

BI Big Data Themen

Analytics 3.0

 

Portfolio

BIG DATA ANALYTICS

THE 7P ANALYTICS PROCESS MODEL

The Big Data Analytics process model by 7P reduces the complexity of big data projects and enables our customers to develop a better, step-by-step understanding of this topic area. Not all techniques and technologies need be mastered from the outset.

  • Digitalisation and technical innovation are frequent triggers for use cases
  • Reducing the complexity when starting Big Data Analytics by decoupling from central points like governance, architecture and analysis
  • Concentration of individual big data initiatives into one Big Data Strategy

Factsheet BigData process model

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OUR BIG DATA SERVICE

Based on this process model, 7P has developed a host of specialised offerings for Big Data Analytics – depending on how mature the topic is in your company

  • Definition of Analytics use cases and pilot projects in a Big Data Need Assessment Workshop
  • Flexible development of Analytics prototypes in the 7P Big Data Lab or on site
  • Selection of analysis platform and definition of architecture blueprintss
  • Definition of a process model and Big Data programs
  • Establishment of an Analytics Competence Centre

Factsheet BigData Program Management

Factsheet BigData Platform selection

Factsheet BigData Lab

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Self-Service-Analytics

Self-Service Analytics as part of a BI & Big Data Strategy

If user expectations are not met, they begin to build their own solutions with their own technologies. Trust in the corporate BI disappears and collaboration with the BI team/BICC is hampered significantly.

With self-service BI, the use of BI software tools is made easier for specialist users and more freedom is given for the generation and evaluation of business information.

Self-Service BI that functions in practice must be based on flexible architecture, methods and organisation. As well as tools, the responsibilities, roles, obligations, methods and processes for SSBI must also be defined.

In a tested 5-day pilot project, we support our customers in stopping the disconnection process and generating quick wins for your company.


Self-Service-Analytics-Grafik


Mobile Analytics

The actual added value of mobile applications is for users who are typically on the move. Their needs quickly go beyond pure analysis – they need native functions, transaction capacities and collaborative components. Our apps integrate:

Our 7P mobile analytics apps integrate:

  • Business Intelligence Dashboards and Big Data Analytics (e.g. algorithm-based Next Best offerings)
  • Native functions such as calendar, GPS, scanner, camera etc. (e.g. in place of laborious manual filtering of analysed products)
  • Transaction-secure functions (during both online and offline operation with secure adapters for the exchange e.g. with the ordering system)
  • Interaction and collaboration (e.g. to enable direct feedback with a specialist at the centre where both see the same content)

We would love to take the next step with you and integrate your mobile analytics apps into a generalised mobile analytics framework. In this way we can solve general technical issues:

  • Security
  • Performance
  • Deployment and
  • Device Management

Mobile-Analytics-grafik


BI/DWH-Modernisation

Nothing is more permanent than change. This is also true for BI and Big Data systems, which must coexist ever more closely and thus grow together.

This is where we apply our services:

  • Predictive analytics on DWH and Big Data
  • Real-time data processing and analysis with corresponding architecture (λ architecture), that enhances historical data with real-time data
  • In-Memory databases as performance layers and abstraction levels overarching classic BI systems and Big Data
  • Data management of classic BI systems (DWH) and Hadoop (HDFS)
  • Enhancement of classic BI systems with Big Data (Data Lake, Data Reservoir)
  • Ascertainment of optimum performance pathways
  • Enterprise DWH platform selection
  • ETL tool evaluations
  • Databank migrations


Business Intelligence & Big Data

JAN-HENRIK FISCHER
BUSINESS AREA MANAGER BUSINESS INTELLIGENCE AND BIG DATA

Get in touch

Phone: +49 221 920070

Partners

  • EXASOL
  • Hortonworks
  • MicroStrategy
  • Tableau