Consulting with Data Stratosphere | niologic GmbH

Data Stratosphere

Forecast potential and monetization in focus

An enterprise-wide data strategy

Forecast potential and monetization in focus

To create a data strategy, we work for you, your consulting projects or your business using established processes. We identify potentials for connecting your data and predictive forecasts. By request we will further work hand-in-hand as your specialists for data science in your consulting projects. We are happy to be your sparring partner in acquisitions.

Our approach to the definition of a data strategy is divided into the areas Define – Measure – Analyze. The optional areas Improve & Control ensure the implementation and monitoring of your data strategy.

Define

Kick-off workshop with your decision-makers and technical experts
In a joint workshop with our customers, we determine existing data sources, analysis areas and contact persons. We define common objectives of analysis, priority and required forecasting capacities. Strategically, we define the future data provision as a cloud or on-premise solution with you. Together we evaluate the current data usage in the enterprise on the basis objective criteria.
Individual interviews
In interviews with the previously determined experts and decision makers, we determine existing data sources and current analytical capacities. We document the existing data based on a raw data pattern. Additionally, you provide us with anonymised sample records if necessary .

Measure

Analysis of existing data
We analyze the existing data focussing statistical significance, validity, timeliness, completeness and consistency. We assess the existing data processes in terms of achieved data quality.
Analysis and documentation of across the enterprise relevant key performance indicators
Preparing possible forecasting methods, we put together the performance indicators in use and document important filter criteria and characteristics.
Statement on the achievement of data quality and on existing data management

Analyse

Defining possible prognosis potential
By exploring your data, we find potentials of prognosis and collate these with existing data. We prioritize these potentials with reference to the possible monetization and rated urgency.
Documentation
You will receive a report on existing potential of prognoses, data structures and data quality. We make recommendations for data management and the use of your prognosis potential. At the same time we define metrics for the measurement of a successful implementation.
Indication of possible forecasts and their monetization

Improve

Implementation of self-service dashboards
On request, we implement prescriptive dashboards, bringing together all existing measures. This enables you to remain agile and achieve a comprehensive overall view of your business as a first data driven initiative.
Implementation of forecasting models
On demand, we implement the recommended forecast models in coordination with your IT consulting and your experts on a singular occasion or as a long term application.
Project management for data management
We accompany your conversion into a data-driven business as a communicator between technical implementation and the agreed business objectives. We constantly compare your requirements with our internal technology database. This way, you gain a demand-oriented data solution.

Control

Control of data and prognosis metrics
After implementation we will assist you by constantly monitoring the achieved forecasting and data quality.

Data Stratosphere

Forecast potential and monetization in focus

Define

Kick-off workshop with your decision-makers and technical experts

Individual interviews

Measure

Analysis of existing data

Analysis and documentation of across the enterprise relevant key performance indicators

Statement on the achievement of data quality and on existing data management

Analyse

Defining possible prognosis potential

Documentation

Indication of possible forecasts and their monetization

Improve

Implementation of self-service dashboards

Implementation of forecasting models

Project management for data management

Control

Control of data and prognosis metrics

Identification of prognostic potential and monetization

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