Applying Data Analysis to Improve Company Workflows and Drive Operational Excellence
Lukkari, Laura (2023)
2023
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2023121938577
https://urn.fi/URN:NBN:fi:amk-2023121938577
Tiivistelmä
Digitalisation and cloud computing have accelerated opportunities to create products and services over the past years. Inspired by the professional experience on customer experience creation in design organisations has resulted to creation of an archetype of customer lifecycle management blueprint and platform stack model that help unpack different key metrics to manage customer lifecycle for a company.
Goal of this thesis was to use the customer lifecycle blueprint and platform stack model as input to research how the company could apply data analytics to improve its workflows and increase operational efficiency by creating data workflows. The research question was ”How can data analysis be used in company workflows to improve operational excellence?”.
The methodology applied was constructive research where a sample dataset was used to build a data storage on Microsoft’s cloud platform. This dataset was used as production dataset sample. The production data was ingested into a data lake set-up for analysis purposes and reporting was automated for team members to be readily consumable on a regular cadence.
The main findings of this research were to collect only data that matters for the designed, needed metrics, use of shared resources to collect the data for the metrics and setting up controls for the cost control when running the data workflows. The customer lifecycle blueprint provided a good overview to the key metrics on the company level as it can visualise the full end-to-end customer journeys and key processes for each team. When building the data analysis workflows it is important to consider using only shared resources for each one of the key metrics needed to keep data quality and cost under control. The data analysis workflows should always be built around shared resources and interoperable platforms to drive operational excellence and efficiency with automated workflows.
Cost efficiency and control are the key aspects when operating on cloud platforms and understanding the respective cloud business model helps control the cost of running the business and workflows. Setting-up available cost controls capabilities between platforms and user groups is essential to keep the line of sight to the cloud computing platform costs.
Goal of this thesis was to use the customer lifecycle blueprint and platform stack model as input to research how the company could apply data analytics to improve its workflows and increase operational efficiency by creating data workflows. The research question was ”How can data analysis be used in company workflows to improve operational excellence?”.
The methodology applied was constructive research where a sample dataset was used to build a data storage on Microsoft’s cloud platform. This dataset was used as production dataset sample. The production data was ingested into a data lake set-up for analysis purposes and reporting was automated for team members to be readily consumable on a regular cadence.
The main findings of this research were to collect only data that matters for the designed, needed metrics, use of shared resources to collect the data for the metrics and setting up controls for the cost control when running the data workflows. The customer lifecycle blueprint provided a good overview to the key metrics on the company level as it can visualise the full end-to-end customer journeys and key processes for each team. When building the data analysis workflows it is important to consider using only shared resources for each one of the key metrics needed to keep data quality and cost under control. The data analysis workflows should always be built around shared resources and interoperable platforms to drive operational excellence and efficiency with automated workflows.
Cost efficiency and control are the key aspects when operating on cloud platforms and understanding the respective cloud business model helps control the cost of running the business and workflows. Setting-up available cost controls capabilities between platforms and user groups is essential to keep the line of sight to the cloud computing platform costs.