Services
Before you invest in AI: check your data first.
Data Quality, AI Readiness and advanced data science, as clearly structured entry products or as an individual implementation.
Many AI projects fail not because of the model, but because of the data foundation. myBytes.com checks in a structured way whether your data is complete, consistent, plausible and suitable for reporting, forecasting or machine learning, preferably in your own infrastructure.
Four packages, clearly delimited.
Fixed price at a clear scope. All prices net.
The quick fact check for a clearly delimited dataset.
Suitable for
- a CSV, Parquet or Excel file
- a database table
- up to 50,000 rows
- up to 200 columns
- up to 10 million data points
Deliverables
- automated data profiling
- missing values per column
- data types and format issues
- cardinalities
- duplicate and outlier indicators
- simple plausibility checks
- risk traffic light
- PDF short report, approx. 8 to 12 pages
- 30-minute closing call
A reliable report for business and IT.
Suitable for
- up to 250,000 rows
- up to 300 columns
- up to 3 tables
- simple key relationships
- up to approx. 75 million data points
In addition to the Snapshot
- review of multiple tables
- join and key analysis
- ID consistency
- time-axis check
- segment analysis
- quality score per dimension
- prioritised problem areas
- concrete recommendations for action
- PDF report, approx. 15 to 25 pages
- 60-minute results workshop
For forecasting, machine learning, reporting and AI projects.
Suitable for
- up to 1 million rows
- up to 500 columns
- up to 5 tables
- one defined use case
- up to approx. 500 million data points, depending on the format
In addition to the Data Quality Report
- use-case relevance
- feature suitability
- target availability, if ML is planned
- leakage risks
- granularity check
- data gaps in time series
- forecasting suitability
- ML and BI risk traffic light
- roadmap before an AI project
- PDF report, approx. 25 to 40 pages
- 90-minute results workshop
For complex data landscapes, multiple systems and higher requirements.
Suitable for
- ERP, CRM, shop, WMS or database data
- multiple systems
- personal or sensitive data
- complex data models
- several departments
- data protection or compliance requirements
- on-premise analysis at the customer
Process
- 30-minute scope call
- review of the scope
- individual proposal
- clear separation of analysis, cleaning and implementation
Three ways to start
From a quick fact check to an individual audit of complex data landscapes.
Data Quality Check
The quick start: we check data quality, plausibility, completeness, consistency and obvious risks.
View packages →AI Readiness Assessment
For forecasting, machine learning and AI initiatives: we check whether data, target variable, granularity and use case are viable.
Check AI readiness →Custom Data Audit
For complex ERP, CRM, shop, WMS or database landscapes with multiple systems, data protection or compliance requirements.
Request a scope call →AI does not begin with the model. AI begins with reliable data.
Anyone starting AI projects without checking the data foundation risks wrong reports, unstable forecasts, expensive pilots and decisions on an uncertain basis. A data quality and AI readiness check creates a clear picture before the investment.
Analysis preferably in your infrastructure.
For B2B customers, trust is decisive. That is why myBytes.com works preferably in an environment provided by the customer. The data does not have to leave the customer infrastructure.
Customer VM
The customer provides a temporary VM. Copies or samples of the data reside there. myBytes.com receives time-limited access. After completion, the VM can be deleted.
Docker analysis package
myBytes.com delivers a reproducible analysis package as a container or script bundle. Execution takes place jointly or by the customer team. The result is reports, metrics and profiling files.
Anonymised samples
For non-critical data, anonymised, pseudonymised or synthesised samples can be provided. Mini samples suffice only for a technical format check, not for a reliable data quality assessment.
Not included in the fixed-price packages
- Data cleaning
- Data modelling
- manual research in customer systems
- productive system integration
- ML modelling
- forecasting implementation
- dashboard building
- legal data protection review
- correction of the data
- ongoing data quality monitoring
The packages deliver findings, a risk traffic light and concrete recommendations. The implementation can then be commissioned separately.
We do not check data superficially. We check along established quality dimensions.
A dataset can look technically clean and still be unsuitable for forecasting, machine learning or management reporting. That is why myBytes.com assesses not only formats, but also usability in the intended business context.
A sample must be representative.
Ten rows out of five million records are not enough for a reliable quality assessment. A sample must contain typical cases, exceptions, periods, categories and known problem cases. For forecasting checks we need historical data over a sensible period, typically 12 to 24 months, depending on seasonality and granularity.
For retail, fashion, FMCG and manufacturing: a reliable sample should contain several products, categories, periods and relevant events such as promotions, seasonal changes, supply problems or production interruptions.
No live data required. No unnecessary data sharing.
myBytes.com works preferably with copies, samples or anonymised data. Personal data is processed only when it is necessary for the purpose of the check and a suitable agreement is in place. The analysis takes place preferably in the customer infrastructure.
Is your data ready for AI?
Start with a clearly limited Data Quality Check. You receive a reliable picture, a risk traffic light and concrete next steps, before budget flows into an AI project. The 30-minute initial call is free.
Let us talk about your data
Free 30-minute initial call. Briefly describe your dataset and your plan; optionally you can state a preferred date.