As datasets continue to evolve and grow in both scale and complexity, enterprises without a big data strategy may not be well-positioned to tackle the coming challenges without sacrificing critical intelligence.
With global market revenue predicted to reach $68.09 billion in 2025, the reality of a big data world can’t be ignored. Helping to facilitate Machine Learning and a litany of other business processes, enterprises across all industries are realising the benefits of utilising big data to enhance their competitive advantage.
But analysing these large volumes of data first requires a big data strategy. Complete with its own set of demands, this differs from a traditional data strategy and can enable enterprises to harness their large and complex datasets to full effect.
Below, we’re exploring how enterprises can do this successfully and the many advantages that it can bring to the whole team.
Why do enterprises need a big data strategy?
With the ability to help users reach previously inaccessible intelligence, big data analytics is recognised as a vital steppingstone for any data-driven enterprise wishing to get the most value out of their datasets.
This capability allows users to find more important trends and outliers across datasets, such as annual performance, as well as to practise complex processes, such as brand sentiment analysis.
A big data strategy determines how enhanced analytics is used to provide benefits such as:
- Better understanding and refining risk management processes
- Empowering HR teams to stay updated on core trends and areas of risk
- Improving overall customer experiences with greater insights on touchpoints
- Optimising field-level organisational workflows for greater efficiency
What are the challenges of building a big data strategy?
While big data analytics promises to bring measurable short- and long-term benefits, it can also present significant challenges.
Without considering these, users may not be able to grasp the full potential of their architecture and may lose vital time and security in the process – factors that enable teams to be truly agile.
Incredibly vast amounts of data can threaten the stability of common traditional architectures, as well as those that lack high-quality data processing tools.
In these environments, analysing 100’s of millions of rows of data at once can require a considerable time investment. This often leaves many enterprises unable to react in the moment to events and can limit proactive approaches.
Data management and governance must also play a high-level role in the face of demanding datasets. Large variation in the types of data – with both structured data and unstructured data being analysed – can lead to significant governance challenges without a functioning strategy.
Building a personalised big data strategy
Building a personalised big data strategy involves the same core considerations as a traditional strategy. Enterprises must also be aware of the challenges and shape their strategy to cater to the different demands.
Enabling data governance
Who is going to be interacting with your datasets, sources, and reports? How can you ensure that access is secure? Who owns these datasets, and where possible, how can you reinforce data privacy?
Tackling and understanding your data governance processes supports more secure, trusted, and reliable results and insights. This is extremely important in big data analytics due to the breadth of data sources, and the potential for highly sensitive information to be included in at least one of these touchpoints.
Catering to architectural demands
Big data analytics requires a lot from your architecture – even more so for agile enterprises wishing to seek results in real-time.
Will your architecture struggle to ingest the large volumes of data needed? Are all datasets and sources correctly configured and integrated?
Posing these questions, among others, can allow teams to recognise if there are additional projects that must first be implemented to make strategic big data analysis a possibility.
Architectural considerations enterprises should focus on when designing a big data strategy include:
- Data collection and ingestion
- Data storage
- Data processing
- Data Governance
Skills and capacity
So, your architecture is fully prepared to take on the significant demands of big data analysis. But what about your users?
As an area which continuously encounters innovation and requires a high level of knowledge to access, big data analytics demands a lot from its users.
If your current BI team or other users of analytics are not updated on the latest best practice, it may damage company-wide insights and inhibit your big data.
Take some time to ask yourself how you can ensure that users are consistently trained and confident in their big data analytics skills. Perhaps you can implement a personal training plan for each user, or bring in outsourced expertise to support your team’s development?
Elevate your insights today
Building your big data strategy requires these considerations to be met and overcome. Without doing so, analytics processes may feel disjointed, unoptimised, and unreliable – causing significant disruption throughout any business.
Our team of users confidently deploy a range of services, operating by a tried-and-tested methodology to reach targeted and trusted intelligence.
To learn more about implementing a market-leading big data strategy for your business, and how to get started, read more about our data strategy services.