Don’t Let Data Complexity Stunt Your Company’s Growth
Over the past few years, data has solidified its status as the currency that drives success for enterprises of all sizes. The ability to quickly gather, analyze and visualize data from different sources empowers business leaders to make faster and more informed decisions. These in turn lead to better product delivery, enhanced customer experience, real-time market feedback and enhanced sales performance. Businesses that are able to leverage data effectively are forging well ahead of their competitors.
On the other hand, many will end up investing a tidy sum in data analytics platforms and get no benefits whatsoever. The key problem here is the sheer volume and complexity of the data. If your business is gathering data from a number of sources, it can quickly become difficult for you to sift through it all, determine relationships and gain meaningful insights, which makes the whole purpose of gathering and analyzing data meaningless for your company.
The Challenges of Complex Data
Data today comes in many forms, shapes and sizes. A whitepaper “on complex data by Ventana Research showed that 71% of businesses gather data from more than six sources, and that 23% use more than 20 sources. It’s not just the size and number of sources that make data complex, however. Structure, level of detail, query language, growth rate and query languages contribute to the complexity of data.
These layers of complexity pose many challenges for businesses, especially in their efforts to effectively leveraging BI tools to generate meaningful insights. But they also present new opportunities, as the growing complexity of data can also provide richer insights into customer targeting, sales lifecycles and operational processes.
Not too long ago, the standard practice was to deploy
dedicated infrastructure and hire specialists to support BI operations. These included data scientists and database server IT architects charged with installing, integrating and maintaining expensive software solutions that run on dedicated hardware. Complex data once called for heavy manual processing involvement to extract, transform and load (ETL) processes, and it often required time and work to model, index and aggregate. Human resource requirements were also multiplied by the need to make changes to the ETL process and data preparation.
Thus, complex data often requires major investment and a significant total cost of ownership for businesses. Complexity can also hurt agility, given the cost of overhead and the time required to prep and analyze data – at least in the case of traditional approaches to managing complex data.
These unsustainable practices have given rise to single-stack business intelligence platforms that provide self-service analytics for data scientists who lack tech savvy, such as the one from Sisense. These solutions simplify BI for complex data by streamlining access to big and disparate data. By optimizing its analytics platform with in-chip and in-memory processing, the solution eliminates the need for expensive infrastructure and data science consultants.
Optimizing Data Analytics for Business Growth
Gartner’s Magic Quadrant for BI and Analytics Platforms report highlights the shift toward self-service applications, citing the business advantages of being able to have independence and complete control over data models and dashboards.
The Magic Quadrant also cites the continuing trend of Software-as-a-Service as a popular deployment model for enterprises, with solutions like Birst providing both private and public cloud deployments, and with IBM shifting focus from its Cognos to the more business-user-centric Watson Analytics.
Here are four major ways that businesses can benefit from the opportunities afforded by complex data:
Streamline data access and integration
Given the volume, velocity and diversity of data, the first step is to deploy tools that make it easy for analysts and non-analysts to access and integrate multiple data sources and produce metrics from these data sets. A rising trend today is the use of platforms that run fast ad hoc modeling of complex data without the need for coding. The aim here is to make it easy for business users to get information easily, rapidly and comfortably, for effective decision-making.
Consider self-service systems
This leads to our next concept: the rise of self-service systems for harnessing data and business intelligence. Different organizational roles will have varying requirements for business intelligence, and this is usually split between IT and analyst roles. Any BI tool deployment will need to not only address these needs, but also provide a way for professionals to work on the analytics platform themselves – without the need for costly manpower or infrastructure.
Optimize for mobility
The main purpose of BI is to aid in agile decision-making processes, and in today’s highly mobile work environment, businesses are placing growing value on the ability to access data, visualizations and analytics from anywhere. While tablets and smartphones have grown ubiquitous, there is a need to optimize visualizations across different screen sizes and bandwidth considerations. Businesses also need to consider platform diversity among their mobile users.
Take a holistic approach to information optimization
The most dangerous enemy of an effective BI strategy is fragmentation. By focusing only on certain sectors or stakeholders in one’s business, you might be robbing others of the opportunity to make better decisions based on data. Thus, this might require a cultural shift among organizations for better data and information sharing, better collaboration among different departments, as well as the use of best practices in managing BI.
Democratizing Complex BI
BI is already being used to improve product marketing, bolster sales, chart user preferences, keep track of the internal operational activities of a business and ensure business growth.
So if you are aiming to give your business a strategic advantage over the competition, you will need to leverage and exploit complex data effectively through comprehensive BI tools that grind down complex data sets into visually appealing, meaningful and useful information.
About the author: Ralph Tkatchuk is a journalist and data security consultant with over 9 years of field experience working with clients of various sizes and niches. Ralph is enthusiastic about helping companies and individuals safeguard their data against malicious online abuse and fraud. His current specialty is in ecommerce data protection.