Companies have started adopting an optimised method for the optimal distribution of resources to carve the path of a company’s growth rather than relying on a trial and error method. The best method of implementation has been incorporating techniques of big data analysis. The business data acquired by large corporations is too complex to be processed by conventional data processing applications. There are better ways to extract useful information which can support proper decision making and help uncover patterns in an otherwise random looking data. These techniques form the core of big data analytics. There are many ways in which small and medium businesses are leveraging big data to obtain the best possible outcomes for their firms.
Small businesses lack the resources to go all in on their big data investments. Therefore, SMBs require a smarter strategy for joining in the big data trend. Here are a few tips –
SMBs can benefit a lot more from big data implementation if they clearly define their goals and do not get sidetracked by the market hype. However, the successes of businesses – large or small – in implementing big data solutions depends requires two things. First, the availability of data, and second, the implementation of right processing technologies.
Now comes the question about how your competitors might be using big data to boost their operations and sales. Well, let’s start with a few prevalent usage scenarios of big data in operations, marketing and sales –
1) Implementing price differentiation strategies: Companies are using customer-product level pricing strategies with the help of big data analytics to achieve targets. According to an estimate, a 1% increase in price can raise operating profits by almost 8.7%. Thus, working out the correct pricing strategy with big data can significantly improve profit margins.
2) Increasing customer responsiveness: B2C marketers are using big data to get greater insights into customer behaviour by using data mining techniques and big data analytics. Proper use of data analytical techniques is necessary in this case. This will help them develop more relationship-driven marketing strategies, prompting greater customers responsiveness and consequently better sales.
3) Big data integration into sales and marketing process: Companies are increasingly investing in customer analytics, operational analytics, fraud and compliance monitoring, R&D and enterprise data warehouses. Nowadays, these are all considered as part of sales and marketing. While customer analytics remains the key area of this investment, evidence shows that developing the other four areas has led to increased revenue per customer and improvement in existing products and services.
4) Embedding AI into big data and its related technologies: The evolving needs of clients and the natural changes brought by big data analytics in sales and service channels has left existing systems gasping for bandwidth while managing tasks. Companies are now turning to artificial intelligence and automation technologies to meet these new challenges. Insights from big data have helped in creating smart and scalable systems which can be used for automated contextual marketing.
5) Using geo-analytics to go after targeted audience: Many companies are now relying on geo-analytical data to focus on their go-to-market strategies. Doing this, they are able to capture territories which have greater sales potential and reduce their go-to-market costs.
6) Search Engine Optimisation and Search Engine Marketing: SEO and SEM remain the two areas where the effect of big data analytics is the most apparent. Data analytical techniques have played a very crucial role in this case. Marketers are betting big on SEO, SEM, email marketing, social media marketing and mobile marketing, and believe that these strategies are the key to long-term success.
7) Pan organisational big data insights: Companies are now switching to big data insights for increasing revenue and reducing working capital costs. Big data analytics is helping organizations become agiler in their operations by introducing scalability at an organisational level.
Despite the belief that big data is only beneficial for larger corporations – which are actively generating massive amounts of data – the fact that big data in itself is useless without data analytical techniques makes a case for the use of data analytical techniques in small and medium businesses as well.
The big data analytics technology is a combination of several techniques and processing methods. What makes them effective is their collective use by enterprises to obtain relevant results for strategic management and implementation. Here is a brief on the big data technologies used by both small enterprises and large-scale corporations.
One of the prime tools for businesses to avoid risks in decision making, predictive analytics can help businesses. Predictive analytics hardware and software solutions can be utilised for discovery, evaluation and deployment of predictive scenarios by processing big data.
These databases are utilised for reliable and efficient data management across a scalable number of storage nodes. NoSQL databases store data as relational database tables, JSON docs or key-value pairings.
These are tools that allow businesses to mine big data (structured and unstructured) which is stored on multiple sources. These sources can be different file systems, APIs, DBMS or similar platforms. With search and knowledge discovery tools, businesses can isolate and utilise the information to their benefit.
Sometimes the data an organisation needs to process can be stored on multiple platforms and in multiple formats. Stream analytics software is highly useful for filtering, aggregation, and analysis of such big data. Stream analytics also allows connection to external data sources and their integration into the application flow.
This technology helps in distribution of large quantities of data across system resources such as Dynamic RAM, Flash Storage or Solid State Storage Drives. Which in turn enables low latency access and processing of big data on the connected nodes.
A way to counter independent node failures and loss or corruption of big data sources, distributed file stores contain replicated data. Sometimes the data is also replicated for low latency quick access on large computer networks. These are generally non-relational databases.
It enables applications to retrieve data without implementing technical restrictions such as data formats, the physical location of data, etc. Used by Apache Hadoop and other distributed data stores for real-time or near real-time access to data stored on various platforms, data virtualization is one of the most used big data technologies.
A key operational challenge for most organizations handling big data is to process terabytes (or petabytes) of data in a way that can be useful for customer deliverables. Data integration tools allow businesses to streamline data across a number of big data solutions such as Amazon EMR, Apache Hive, Apache Pig, Apache Spark, Hadoop, MapReduce, MongoDB and Couchbase.
These software solutions are used for manipulation of data into a format that is consistent and can be used for further analysis. The data preparation tools accelerate the data sharing process by formatting and cleansing unstructured data sets. A limitation of data preprocessing is that all its tasks cannot be automated and require human oversight, which can be tedious and time-consuming.
An important parameter for big data processing is the data quality. The data quality software can conduct cleansing and enrichment of large data sets by utilising parallel processing. These softwares are widely used for getting consistent and reliable outputs from big data processing.
Big data analytics plays a significant role in organisational efficiency. The benefits that come with big data strategies have allowed companies to gain a competitive advantage over their rivals – generally by virtue of increased awareness which an organisation and its workforce gains by using analytics as the basis for decision making. Here is how an organisation can benefit by deploying a big data strategy –
Big data solutions help in setting up efficient manufacturing processes, with demand-driven production and optimum utilisation of raw materials. Automation and use of AI to reduce manual work is another way of achieving cost efficiency in production and operations. Further insights into sales and financial departments help managers in developing strategies that promote agile work environments, reducing overall organisational costs.
Data-driven decision making is helpful in boosting confidence among the employees. People become more pro-active and productive when taking decisions based on quantifiable data instead of when asked to make decisions by themselves. This, in turn, increases the efficiency of the organisation as a whole.
As evidenced earlier in this post, creating differentiated pricing strategies are known to help develop competitive pricing and bring in the associated revenue benefits. Also, organizations can tackle competing for similar products and services by using big data to gain a price advantage.
Demographics divide most markets, but there are even deeper divides that exist in customer classification. Big data analytics can help categorise customers into distinct tiers based on their likelihood of making a purchase. This gives sales reps more solid leads to follow and helps them convert more. Furthermore, when sales and marketing are based on big data insights, it is likely that the sales reps are intimated with a potential customer’s tendencies and order histories – driving up the rep’s advantage.
Customers are likely to respond more to relationship-driven marketing. Using data analytics, organizations can leverage their prior knowledge of a client’s needs and expectations and offer services accordingly. Thus, significantly increasing the chances of repeat orders and establishing long-term relationships.
Using big data technologies has become a useful tool for HR managers to identify candidates by accessing profiled data from social media, business databases and job search engines. This allows companies to hire quickly and more reliably than traditional hiring techniques which always have an element of uncertainty. Also, when organizations are using analytics across all platforms, it becomes imperative for them to hire candidates who are in sync with their policy.
Big data strategies not only provide better decision-making powers to organizations but also give them the tools to validate the results of these decisions. Organisations can recalibrate their strategies or scale according to newer demands using these tried and tested business strategies.
Our years of experience state that businesses that combine their strategies with corresponding big data analytics solutions can gain a significant competitive advantage and position themselves for success in a data-driven world.
There is no doubt that Big Data technology will continue to evolve and encompass more fields in the coming years. As the rate of data generation increases, even smaller enterprises will find it hard to maintain data sets using older systems. Analytics more than anything will become the guiding principle behind the business activity. Moreover, companies will need to be more automated and data-driven to compete and survive. The evolution of artificial intelligence with technologies like machine learning and smart personal assistants is also heavily reliant on big data. The role they will play in the future of business management, manufacturing processes, sales and marketing, and overall organisational remains to be seen.
However, the promised utopia is still a good time away, and it is not too late for businesses to start investing in data analytics technologies and ready themselves for the future. As the technology becomes more common it will certainly become less expensive to implement. But considering the rewards, early adopters of the technology will surely become its major beneficiaries too.
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