Effective data management delivers better business insight, creates a more positive business impact, increases process velocity, reduces risks relating to information governance and addresses information security concerns. The following information will provide you with expert tips that will help enterprises to develop an effective data strategy:
Identify key areas that need to be addressed.
Start the process of developing an effective data strategy by assessing your organisation’s current data management maturity. Once you understand where you are at, in broader terms, then you will then have a better grasp of where to go from a data management point of view.
- Looking at what the business wants to achieve through the management and analysis of data;
- Putting together a definitive strategy based on your specific needs.
What you may find, after undergoing this evaluation process, is that you don’t need a bells-and-whistles data management strategy, as there is no business requirement for this, and that artificial intelligence (AI) capabilities can be retained for business applications.
Turn your data into a business advantage
Once a data management exercise has been completed, a critical step in developing an effective data strategy is to understand exactly what it is that the business expects to get out of the strategy. The business assessment helps to pinpoint how a company should leverage data within different areas of the business.
For instance, a more complete view of data can allow for:
- A deeper understanding of buying patterns for a consumer-facing company;
- More targeted upselling;
- Better intelligence for management; and
- Ultimately, more revenue.
Assign data to data tiers
Data tiering comes in to play once you have a clearer understanding of what the business’ data needs are, which data is critical, and which less so. For example, data received via sensors within the manufacturing plant of an automotive company is important, but should not be cross-pollinated with business data.
Develop data protection, governance and redundancy policies
When developing data protection, governance, and redundancy policies, certain principles must be taken into consideration to develop an effective data strategy.
These include the following:
- Lawfulness, fairness and transparency: Data must be processed lawfully, fairly, and in a transparent manner;
- Purpose limitation: It must be collected only for specified, explicit and legitimate purposes;
- Data minimisation: It must be adequate, relevant, and limited to what is necessary in relation to the purposes for which it is processed;
- Accuracy: It must be accurate and, where necessary, kept up-to-date;
- Storage limitation: It must not be kept in a form that permits the identification of data subjects for longer than is necessary for the purposes for which the data is processed; and
- Security, integrity and confidentiality: It must be processed in a manner that ensures its security, using appropriate technical and organisational measures to protect against unauthorised or unlawful processing and against accidental loss, destruction or damage.
Automate and optimise data ingestion
Clean data is critical to powering those systems, such as business intelligence and predictive analytics, that will bring value to your business. The days of data extraction and cleansing being handled manually are long gone; there is simply too much data, and it is too varied to be processed by individuals. Data quality is king, so it makes sense to automate the ingestion process, with the use of machine learning (ML) and AI algorithms. This can help organisations to get a handle on huge volumes of data, and allow for the introduction of data governance, boosting data quality and making for more meaningful business use.
Augment analytics by integrating AI elements into the analytics and BI process
Augmented analytics is the next paradigm shift of data democratisation, when compared to the current self-service business intelligence (BI) tools and analytics. By integrating AI elements into the analytics and BI process, augmented analytics presents a streamlined user experience, from data ingestion and insight discovery, to understanding correlations in data, and platform interaction.
Explore blockchain for your data and analytics
The question of blockchain links into the points made on data ingestion. The highly secure nature of blockchain allows for greater data governance. Blockchain provides data transparency, but does not allow for editing, which means that data integrity can be verified at any point in the chain. It can also be used for both structured and unstructured data.
Blockchain deals with data – analytics uses data for actionable insights, while blockchain records and validates data.
Some major challenges to data in analytics of blockchain data include:
- Inaccessible data
- Privacy issues; and
- ‘Dirty’ data.
Benefit your data analytics with natural language processing (NLP)
Natural Language Processing (NLP) helps unlock the value in unstructured data; that is, turning input text into structured data. NLP tasks, such as sentence segmentation, part-of-speech tagging, named entity extraction, topic modelling, or text summarisation, enable efficient and accurate analysis of content, which support an effective data strategy.
Professional data management services
Datacentrix is a market leader in data management. Businesses need accurate, timely and relevant information to plan, allocate resources and manage the business to meet their strategic objectives. Information assets that are effectively structured and managed enhance efficiencies, promote transparency and enable business insight.
Speak to our experts today for more information on developing an effective data strategy:
- Data management services
- Technology-agnostic consulting services
- Data assessment and strategy
- Data governance
- Data architecture
- Artificial intelligence
- Social business
- Implementation methodology
Contact Shakeel Jhazbhay, General Manager Digital Business Solutions at Datacentrix by emailing email@example.com