Want to find your way from data problems to data potential?
09 | 2024 Olli Laine, Co-founder & CEO
Have you noticed that when discussing data, the conversation quickly turns to typical challenges such as data quality, architecture or exploitation - rather than the opportunities it offers?
I compiled the most common challenges and alternative ideas that can help us guide our clients towards a culture where data discussions focus on real competitive advantages brought by data, rather than problems, and on the smooth utilization of data to support AI.
1. Modern data architecture for growth and innovation
Many companies struggle with data fragmentation and complex infrastructures, making it difficult to manage and use data. When data is stored in different systems and spread across different parts of the organisation, it becomes difficult to manage and slow to use. Typical challenges include system fragmentation, where data is scattered across different systems without easy integration, and legacy systems that do not support the processing, analysis or real-time access of large amounts of data. Or with the current data platform or legacy data warehouse, time-to-market is too slow/working/expensive e.g. when integrating new sources or producing new business reports.
The solution is a modern, scalable data architecture that is moving to the cloud. This will allow data to be centralised on an easily managed platform and use technologies such as Snowflake and Google BigQuery for powerful analytics. API-based integration connects the systems, making data quickly accessible. The simplified architecture enables faster and more reliable data analysis, promoting real-time decision making and the adoption of new technologies such as artificial intelligence, allowing companies to react faster to changes and stay ahead of the competition.
2. The important role of the Data Engineer & the right tools for the right problems
You need the right people and tools to manage and use data. The role of the Data Engineer is central to the collection, movement and storage of data. Without an effective data engineering process, data exploitation is slow and unreliable.
Typical challenges include a lack of efficient tools, which can slow down the transfer and processing of data. In addition, the fragmentation of work can lead to bottlenecks and delays in data exploitation, especially if data engineers do not work closely with other teams.
Our data engineers' modern tools, such as Apache Kafka for real-time data transfer and Databricks for processing large data sets, are a critical part of the solution. Airflow helps with data orchestration and automates the flow of data between systems, ensuring fast and reliable data collection, transfer and processing.
As a business benefit, the right tools and smooth processes improve data utilisation, which in turn improves the quality of analysis and speeds up decision-making. This supports business agility and competitiveness.
3. Quality data - the basis for business decisions
Data quality is critical to the success of a business. Poor quality data can lead to incorrect decisions or a lack of trust in the data, which can lead to significant costs and a loss of competitiveness. Common data quality problems include missing data, duplication, inconsistencies and incorrect values.
Ensuring good data quality starts with an effective data strategy. This means managing and controlling data through consistent processes, rules and monitoring, ensuring that data is accurate, high quality and usable. It is also important to note that not all data has to be of high quality. Focus resources primarily on the data that is most relevant to the business and strategy.
High-quality data enables more accurate and reliable decision-making across all areas of business, and the potential to truly harness the benefits of AI. Trust in data helps business leaders to optimise processes, improve efficiency and predict trends, which in turn leads to better customer solutions and increased competitive advantage in the market.
Accelerate the potential of AI by getting your data right!
With the right data quality, architecture and data engineer tools in place, our customers can make the most of their data, especially the most critical data. Data then becomes a business driver, helping to anticipate trends, make better decisions, guide software development and develop new services. These are themes that every business leader should consider, both from the perspective of their own business and the capabilities of their competitors.
At Kipinä, the data used by our customers has provided a significant competitive advantage, especially in the use of artificial intelligence and machine learning in digital services. Even if your company is not yet using generative AI or machine learning, their use will become essential. In this case, what separates the winners from the losers; data architecture and its quality!
By ensuring you have quality data, an architecture that supports growth, a process and the right tools for data users, you can create a strong foundation for future competitive advantage - with data security in place.
Want to know how to get the most out of your data and strengthen data-driven leadership in your business - contact us!
Check out our Planet AI artificial intelligence case study!
Olli Laine, Kipinä Co-founder & CEO
The author is one of the visionaries of the future of Finnish digital development, a passionate advocate of data-driven leadership and one of the co-founders of Kipinä Software. In addition to his CEO hat, he wears an orienteer's headlamp and a skier's hat.