Analytics & Data-Driven Solutions
With the amount of data rapidly growing throughout the world, it is becoming next to impossible to compete in almost any industry. Unfortunately, we are witnessing the fact that BI and Data-Driven approaches are struggling to catch up due to data dispersaty, quality and ease of extraction.
Overview & Challenges
The amount of data is growing every day and almost every business has to work with a lot of it. Unfortunately, data tends to pile up quickly and in multiple places due to the use of different third-party solutions and data collection methods (manul and paper including), while the need for its analysis and comparison is important to drive growth and make crucial data-driven decisions. Also, the rapid adoption of machine learning as well as the drop in its cost is driving ROI for having data stored and processed correctly to get new and sometimes game changing insights.
- Data collection and ETL (extract - transform - load)
- Data normalization, matching and quality control
- Data enrichment from other sources
- Data security (at rest and in transit)
- Data warehousing (data lake, data mart)
- Big data schema optimization for perfomance
- Data analytics and event triggers
- BI and machine learning
Approach & Technology
We utilize a result-driven approach in dealing with data, so we initially work to define what the data needs to be stored and processed for and then develop the flow from there, while always keeping focus on the results. Also, most of the time the data holds valuable insights that might not be clear at the start and we always try to understand the business goals to help with the definition of the possible scope and projected ROI. We strongly believe in the data-driven approach to doing business in the 21st century and help clients expand automation to gather more data as well as enrich it with data from third-party sources.
In terms of technology there are currently countless solutions on the market that need to be carefully assessed per specific scope requirements. The list of possibilities is constantly growing and we always monitor/assess new products, but here are are few technologies we have successfully utilized in the past:
- AWS Data Warehouse stack (Kinesis, S3/Glacier, Redshift/Athena)
- Microsof Azure Data Warehouse stack (Data factory, Blob, SQL Data Warehouse)
- NoSQL Data Warehouses (MongoDB, DynamoDB)
- Google BigQuery
- Replication, Sharding, Clusters
- Custom built / open source data scraping and ETL soluitions
- Cloud/Service ETL (AWS Glue, Azure Data Factory)
- Custom built / open source BI soluitions
- Cloud/Service BI (PowerBI, Tableau, Domo)
- Machine Learning algorythims (Regression, Perceptron, Recurrent neural networks)
Outcome & Effect
Even first minor steps to data driven decision making tend to make a huge impact on business growth and operational predictability. Marketing, operations and strategic planning – all greatly benefit from data analysis, providing big competitive advantages and driving internal innovation. Hidden insights can open up new opportunities and empower key pivots for rapid expansion. KPIs and goal clarity increase staff performance along with moral and help HR departments make key decisions. Machine learning opens up intricate dependencies that can radically change strategic plans and expose investment opportunities.
Outcomes of shifting to data driven approaches cannot be overestimated. Here are some real life changes we have witnessed:
- Internal company meetings are held around charts and diagrams
- Staff members understand clear and measurable KPIs
- Key decisions are made quickly and have a firm basis
- Marketing ROI grows exponentially
- C-level executives spend less time on assessment
- "Why?" questions almost dissapear
- Market change reaction time is minimalized
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