Understanding your Data
Data is what makes the world go round, especially the digital one. It is therefore important to understand, consolidate and automate your data to maximise its value. Leveraging your data can help an organisation retain a competitive advantage, retain customers and grow business.
However many organisations are faced with a tangled web of unstructured data that is scattered across systems causing silos.
To leverage data it is important to know what you have in your system. The data could be customer, behavioural, asset or process data. Then understand what the data means and how it works within your business and automated workflows.
A solid data strategy can help an organisation focus on data consolidation and automation. As well as help map out suitable data-driven business models that can help inform data-driven decisions.
Siemens suggests that leveraging data to create value requires an organisation, culture and processes to be integrated or run parallel to the existing core business.
How best to obtain and consolidate data across silos
Firstly, data silos are isolated groups of data that occur when an organisation grows and departments split into specific teams. If barriers between teams keep growing the information shared can be very thin. This is when data silos start to form.
Data consolidation combines and stores multiple data sources in a single place, such as a data warehouse or database. Consolidating data and implementing processes can help turn data into valuable, accessible insights that influence effective decision-making.
Now emerging is the concept of a data lakehouse. What is a data lakehouse? It is the combination of data warehouses which are repositories for organised data used in analysis and data lakes which manage both organised and unorganised data, particularly for advanced analysis. Lakehouses merge these two concepts, providing analytical versatility with a wide range of data formats.
The best ways to obtain and consolidate data across silos is by:
Consolidate Data Management Systems
Identify what systems and processes are contributing to your data silos. Then audit them to collect information on what is in use, and who is using them and evaluate if these systems generate relevant data for the organisation.
Create a Collaborative Company Culture
Embrace a culture of cross-departmental communication and work to help break down data silos. In creating a collaborative company culture you will bring together and celebrate a range of people. All of these can share diverse ideas, knowledge, skills, experience and resources to innovate and solve problems. This makes it much easier for employees to want to share data with other teams.
Use Integration Software with Native Integration Capabilities
Using an integration solution can help locate and move data between your different systems. Building integrations so applications can communicate and share information correctly is one of the most effective ways to remove and avoid data silos.
Embedded iPaaS and iPaaS are powerful tools designed with native integration capabilities and allow dynamic two-way integrations. You’ll be able to detect common use cases and create bespoke solutions to connect the internal data with other systems. Providing your organisation with high-quality data that maximises its value.
Be Consistent and Incorporate in your Company Strategy
As well as a company culture of collaboration you should incorporate it and integration as part of your company strategy. The organisation will continue to obtain and consolidate data across departments in a consistent, collaborative fashion.
With data automation, you’ll have high-quality databases enriched by all the applications you use across your business. Providing you with a unified view and helping your teams make informed decisions and improve processes company-wide.
How can data be analysed and optimised to promote automation?
Whether drowning in a data lake or stuck in a data silo the information will be hard to analyse for any human. Due to the unstructured nature of the data, it will take significant time to analyse. This has a domino effect on decision-making, customer experience and so on.
Automated processes can analyse and optimise the data faster and maximise value from data. Data integration means information is sent to relevant systems for easy access for the right teams in real time. With this streamlined process, employees can get on with other important tasks, the risk of human error is removed, manual processes are eliminated, business decisions aren’t delayed and customer experience is improved.
By implementing data automation it can bring both scope and depth to insights, and enable products to be tailored to the needs of customers. As well as create and improve the transparency of big data.
Best Practice to Maximise Value from Data – Utilising the newest tools and advances in technology
When deciding on tools and technology to maximise your data’s value it is important to choose tools that keep pace with you and your customer’s needs.
Automation and integration tools are able to adapt and support your best data practices as you grow. A long-term solution and help business leaders with data visualisation, data discovery and analysis, in the decision-making process.
These tools are typically within a cloud-based platform like a SaaS or iPaaS. Users can utilise the cloud technology for development and data storage, as they can scale well and process complicated queries faster. As well as simplifying the combination of different datasets.
The use of AI and machine learning with data is also increasingly common as they can help identify data types, find commonalities within datasets and be used to automate and accelerate data preparation tasks.
Ultimately it is important to use tools and technologies that are aligned with your business goals, customer needs and long-term plans. Implementing a new technology and new business processes across an organisation takes time so it is important to take into account as many considerations as possible to maximise the value of data. The usability, integration, capabilities and compliance are just a few considerations.
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