Finding insight in an increasingly data-driven world
Data is more accessible, cost-effective, and available than ever before. From consumer research and email signups to clicks on a page, browsing history and connected ‘things,’ the opportunity to collate, examine and inspect data for competitive advantage for your business – be it for operational efficiencies or commercial insights – is greater than ever. This process of drawing insights and value from collected data to inform strategies, decisions, and priorities is broadly categorised as Data Analytics.
However, successful Data Analytics efforts involve a lot more than collecting data in one location and making decisions based on possible insights. A core concept of any successful analytical effort is in possessing an overall Data Strategy.
The importance of a Data Strategy
A Data Strategy involves and instructs multiple areas of data within a business, from how it is collected, stored, utilised, and maintained. Within data analytics, a successful strategy will ensure that incoming data is high-quality, uniform, and provides an overarching view of operational and commercial strategy and execution without introducing silos or a bias. In driving value throughout the entire data process with a successful strategy, businesses can ensure that their analytics are reliable and relevant.
Data Analytics derives value for an organisation based on current, available data. While this is possible, it’s not necessarily always the best principle. For those wishing to derive the greatest value possible from their data, begin by asking ‘What are the greatest value and the most beneficial insights I could hope to gain?’ From the answers to this, it’s possible to implement an overall culture that runs throughout the entire business, fuelling insights relevant to the overall goal.
Through incorporating successful Data Analytics efforts, businesses can expect a wide range of benefits that combine to provide leverage over other competitors. To accommodate this demand, analytical capabilities have become more advanced and agile than ever before, seeking to enable businesses to maintain leverage with cutting-edge technology and software.
What can a structured Data Analytics approach bring to any business?
As we’ve already discussed, Data Analytics is the pathway to deriving value from your current datasets. This can then be used in numerous ways to drive operations and strategy. This is considered the primary benefit of Data Analytics – the ability to instil confidence and reliability in strategic and tactical decisions through the correct use of data.
However, there are many other benefits that businesses considering incorporating Data Analytics should be aware of. Some of these include:
- The overall increase in productivity
Many analytical tools, such as Machine Learning and predictive analytics, feature heavily automated capabilities to navigate complex algorithms and equations with ease. This would traditionally involve a lengthy, unavoidable manual process. Through these automation techniques, a data analyst may be able to devote more available time to other ventures that encourage growth and development.
- The reduction or elimination of data silos
With an overarching data strategy that feeds into the analysis software. Businesses can eliminate the construction of data silos – separate pockets of data that, when missing from a particular analysis, can shift insights with unintentional bias. A well-communicated strategy eliminates the need for separate datasets, generating trust and reliability in current insights and providing a clear overview of data in one, centralised location.
- The creation of a proactive approach
With the right foundations for data collation in place, Data Visualisation tools allow businesses to achieve rapid insights on demand, often in real-time. This allows businesses to take a proactive stance. Businesses are now able to review historical data and lasting trends alongside potentially real-time data collated from a variety of sources to achieve much richer insights, trends, and decisions than ever before.
What are poor Data Analytics practices and what are their consequences?
For those businesses wishing to increase their data-driven insights, there are many poor practices that they should be aware of. If not, the consequences on their overall operations may not be as beneficial as first hoped and therefore not have the business impact desired.
For many businesses, their approach to data analytics involves complex manual processes based on current data. If not rectified, this may result in unnecessary frustrations, complications, and time spent that could be better utilised elsewhere. This may be visible in the manual processes of a retailer, where the incorrect entry of a barcode suddenly shifts incoming revenue by thousands. Although incorrectly informing local performance, this may also be harmful to overall operations through incorrect reporting to key decision-makers.
Another example of poor data analytics occurs when this same principle of relying on manual processes are applied to data storage techniques, such as designing unoptimised or underperforming ETL processes. Here, data may be incorrectly stored or processed, potentially leading to duplicates, inconsistencies, and inaccuracies.
These examples illustrate how important not just correct analysis processes are to a business’s function, but also how crucial consistency is. When data is mismanaged or misused, the overall effect on all areas from local storefronts to key stakeholders and strategies are affected.
Leading on from this, it is important that when businesses choose to implement a Data Analytics strategy, they take the necessary steps to ensure that their data is consolidated and implemented effectively and consistently. Choosing where their data is stored may also be a key concern, as the rise of Cloud-hosted data has enabled a level of flexibility and scalability previously unachievable.
Cloud-hosted data and its benefits
One of the most important recent developments in data storage, analysis, and management, is Cloud computing.
Historically businesses had to utilise dedicated Data Warehousing hardware either on premise or in a data centre. These solutions were often expensive and involved the maintenance of a physical location. However, with the rise of Cloud-hosted solutions with facilities such as Azure or AWS, businesses now have the opportunity to store their data in an on-demand environment regardless of location.
A core benefit of Cloud-based hosting is in its ability to directly scale with the data that is being stored. Instead of organisations having to buy new storage and hardware, as well as having to find a physical location to hold it, a Cloud-based approach allows automatic unlimited scaling of facilities as collected data begins to grow and expand.
Cloud storage has evolved to become a contemporary necessity. Instead of purchasing a CPU that remains dormant 75% of the time, Cloud storage demands that you only pay what you use it for, and with an increase of cloud-based cybersecurity, capabilities, the initial concerns of storing data in an unknown location has now dispersed.
With the arrival of the Cloud, businesses are subject to a wealth of advantages specifically tailored to promote cost-effectiveness, scalability, and unparalleled flexibility. This is crucial when considering Big Data – a particularly challenging and demanding aspect of Data Analytics that often pushes conventional on-premises systems to the limit.
Introducing Big Data
One aspect of Data Analytics that has gained considerable ground in recent years is Big Data. Although commonly used to refer to incredibly vast datasets that are made of billions of rows of data in numerous locations, there is much more behind the term that demands attention.
While Big Data may still be used in this manner, it is worth noting that Big Data may, and should, also be used to refer to data that is, while not notably vast, very complex. Unstructured, dispersed, or data of different types are all examples of this, and navigating Big Data to derive value can provide unique challenges. Some of these include:
- Due to Big Data being stored in various forms and locations, it can often be difficult to gain overarching insights without a functional infrastructure.
- Implementing the correct tools to analyse hundreds of millions of rows of data can be challenging and demanding for unoptimised systems.
- Due to data being stored in a wide number of locations, overall data security may be weakened, potentially leading to expensive and damaging data breaches.
Another term that’s slowly being incorporated alongside Big Data is Wide Data. This may refer to vast amounts of information on one specific entity, such as a specific product (i.e. ingredients, suggested stock, RRP, allergens etc) without vast sets of data. An example of this may be a small customer database, where a wide range of data is stored on each customer, while the range of customers targeted is still low.
In cases concerning Big or Wide Data, special considerations must be made to accommodate these demanding factors to ensure that insights gleaned are overarching, reliable, and void of bias.
Big Data Solutions and Data Architecture
As data becomes more vast, complex, and available, solutions and strategies have adapted to provide ongoing support. Two forms of data architecture can be used to accommodate unstructured datasets:
- Data Warehouses: A single location aimed at providing a complete overview of the complete data held by an organisation.
- Data Lakes: A pool of unstructured data in its raw form. Data stored here is ready to be pulled into other software for analysis and interpretation.
These new forms can store unstructured and complex data in a centralised location, before integrating them alongside conventional forms of data to gain overarching insights.
When considering implementing modern data architecture strategies such as these, it’s important to ensure that a data-driven culture pervades throughout the entire operations – further ensuring that silos are eliminated while focussing attention on how data can be utilised to inform, rather than acting as a subset of IT, which is no longer correct.
To learn more about the importance of a data-driven culture within business, read our blog here.
The emergence of Machine Learning
Alongside Big Data, another development that is becoming more widely deployed and utilised is Machine Learning (ML). While the ability to perform machine learning algorithms has been around for a while, ML’s ability to model and undertake rapid processes with Big Data is one of the many recent ML developments.
When incorporated into a data strategy, Machine Learning allows users to create complex models that, when applied to the entire data analysis landscape, enable extremely complex processes to be performed rapidly and autonomously, providing comprehensive insights.
A great example of ML’s use and advantages include specific processes such as sentiment analysis – an often-overlooked manual process that involves the crawling of unstructured social media data to gain insight on public sentiments concerning an issue or organisation. Using Machine Learning, this process can be automated through the creation of a specific model.
The primary advantage of any Machine Learning approach will always be efficiency, automation, and reliability. Using effective, well-designed models, ML can quickly deliver required information on demand, allowing data analysts to focus on other pursuits and areas which require more attention.
Though Predictive Analytics has existed for years, recent advancements in capabilities have ensured rapid adoption for many businesses. Predictive Analytics uses algorithms, Machine Learning processes, and historical data to predict future outcomes with the intention of informing critical business decisions in advance.
Core use cases of Predictive Analysis are:
Based on historical user data and patterns such as login behaviour and location of access, Predictive Analytics can detect suspicious behaviour that would be indicative of corrupted credentials or fraudulent activity. With the increased adoption of Cloud based data and renewed interest in remote working cases, the use of Predictive Analytics is increasingly important.
- Optimising campaigns and activity
Predictive Analytics enables businesses to model future outcomes based on customer behaviour, determining the response to various promotions, sales, or other marketing efforts. As the competition between businesses grows fiercer and fiercer, this is a tool being utilised more and more in an attempt to secure leverage.
- Improving productivity within businesses
Businesses wishing to improve productivity and efficiency within their internal operations may use Predictive Analytics to determine which factors influence productivity and development within their internal environment, before experimenting with variables to optimise results.
Whilst Machine Learning and Predictive Analytics have been around a while, increased advancements within the Data Analytics landscape has ensured renewed focus on these tools, allowing for greater adoption to drive results in a number of use cases and bringing greater insights and competitive advantages to any business.
For businesses that believe that ML is the next development in their ongoing data strategy, we encourage you to contact us to discuss how to gain the most value possible from this upcoming advancement.
The future of Data Analytics
Data evolution continues to be a fast moving and ever-developing industry. As new technology is released and realised, capabilities are impacted, and systems are forced to optimise. Looking to the future of Data Analytics, we here at DataShapa expect to see some of the following, among other features:
- The role of a Data Specialist becoming an integral part of any organisation that incorporates a data strategy as analysis capabilities become more advanced and effective than ever before.
- A wider embedding of a data-driven culture that is founded on more readily available quantities of data in any form.
- The increasing standardisation of sector-specific data analysis practices.
- The decrease in the acceptable margin of error between competition due to an increased reliance on data to fuel decisions.
The difficulty of incorporating data analytics and how DataShapa can help.
At DataShapa, we’re committed to driving value through the effective use of data – whether through management, storage, or analysis. We’ll work alongside you to observe your current data objectives, before working alongside you to optimise your current landscape to empower analysis and achieve insights tailored to give your business the competitive edge.
For more information, why not read about our previous data analysis projects, such as our work with TSS here. For any queries or questions you may have, contact us here.