Data-Driven Decision Making for Small Businesses with Blockchain
How Can Blockchain Applications Work with IoT, Machine Learning, and Automation to Help Small Businesses Make Use of Big Data?
Why Big Data Analytics Are a Competitive Necessity
The concept of using data to foster internal improvements within a business might seem like a no brainer. After all, statistics and data analysis have always been a cornerstone of business development. So it seems fair to assume that modern day businesses would be able to piggyback on the analytical foundations laid out before them.
In reality, most organizations are still struggling to grapple with the advent of enormous data surpluses born out of the rise of the internet and its connected technology. With the rise of IoT, evolutions in database tech, and unprecedented visibility into consumer behaviors, creating cultures of data-driven decision making has become exponentially more complicated. Regardless of the difficulty, proper practices for recording and analyzing data are necessary adoptions for businesses hoping to remain competitive.
The Potential Value of Data is Largely Being Left On the Table
Unfortunately, the mere registering and reporting of data is only the first step to obtaining the proposed value of this resource. According to a 2016 McKinsey report, “the biggest barriers companies face in extracting value from data and analytics are organizational.” Gathering, storing, and even analyzing data is much easier than integrating the subsequent analytical optimizations into the day-to-day operations of a business. Essentially, most businesses are doing an okay job of collecting and parsing data, but are failing to implement real changes based on there analytics.
This is a problem that the combined forces of IoT, blockchain, machine learning, and automation can potentially correct. When used in this way, these tools can rightfully be placed under the umbrella term of business intelligence (BI). With only 10–20% of the proposed value of data analysis currently captured in US health care and EU public sector, 20–30% in global manufacturing, 30–40% in US retail, and 50–60% in location-based data services, the need for new models of BI is clearly evident. And while we will be discussing how blockchain-integrated BI services can help obtain more value from data, we should first cover some of the basics of big data and its ongoing evolution.
The 4 V’s of Big Data and Problems with Traditional Systems
Volume, variety, velocity, and veracity. These are the 4 V’s of big data and are at the basis of most correlated analytical solutions. Traditionally, BI and associated data analysis was something that only the top tier organizations could afford. Nowadays, cloud-based services and other data-driven innovations have enabled even small businesses to collect large volumes of information with relatively high variety, velocity, and even veracity.
The problem with traditional systems is that they largely functioned by responding to data retroactively. An organization would receive statistical analysis of their operations during a specific timeframe and attempt to adjust their day-to-day ongoings according to potentially stale or otherwise unreliable data. Modern BI approaches and tools offer organizations of all sizes with opportunities to act on data in real-time. Extracting value from a real-time data stream is then dependent on creating the appropriate infrastructure and culture to make both manual and automated decisions regarding daily operations based on that analysis.
A Closer Look at Database Technology
BI used to involve corporate officers holding physical reports filled with with charts and graphs that mentioned KPIs and other seemingly significant vocabulary. While we have collectively gotten better at separating data wheat from chaff, this is still a fairly common scene and it generally involves too much information without enough real analysis or actionable material. Fortunately, as the 4 V’s continue to expand and traditional data infrastructure grows increasingly outdated, new products for data warehouses, dashboards, and visualizations have become more commonplace.
Data warehouses are the logical progression from their less robust storage predecessors. These came in the form of centralized relational databases built using SQL language that essentially aggregated everything from formerly disparate data stores into a single location. The purpose of data warehouses is to answer analytical queries that are beyond the scope of less voluminous storage options. An additional layer of dashboard and visualization applications have enabled businesses to look within their data warehouses and understand some of the information from a BI perspective. These tools have collectively formed the process of diagnostic analysis.
From Diagnostic to Predictive/Prescriptive
With diagnostic analytics, organizations can interact with their large stores of data and determine exactly why things were happening at various scales within their operations. This is a major step in the right direction, however it still mostly involves a retroactive approach toward optimization. It’s also become increasingly costly and inefficient for businesses to migrate data from disparate silos into a single data warehouse, through a process known as ETL (extraction, transformation, and loading). As new innovations in IoT and tracking software foster the continuous growth of the 4V’s, ETL is now a major pain point in the analytics process.
This is why organizations interested in utilizing big data have begun to migrate from relational databases built with SQL towards linearly scalable alternatives built with Hadoop and NoSQL. Without getting too in the weeds about these technologies, we can essentially attempt to understand this development as a shift away from centralization of data toward distributed, cloud-based storage options.
And with larger, more decentralized pools of data, businesses can run software that identifies larger patterns and helps create predictions and preemptive optimizations within a system. This is how the new realm of predictive and prescriptive analytics has come to the foreground.
The Latest in Decentralized Data
One of the more recent developments in decentralized data technology involves blockchain distributed ledgers. Blockchain databases are scalable across multiple networks and use cryptography to provide both secure and transparent systems for transactions and information exchange.
The transparency and immutability associated with blockchain-based data registration offer vast improvements to the veracity of the 4 V’s. With decentralized cloud-based infrastructure, the volume of data can also be vastly increased without typical infrastructural costs. And the variety of data is then improved by newer tracking tech in collaboration with these cryptographic decentralized ledgers.
A Perfect Symphony of Data-Driven Tech
Machine learning and artificial intelligence tools are currently being developed to turn the noise of big data into actionable information and analysis. This is the backbone of the predictive and prescriptive analytics evolution. When coupled with automation, real-time optimizations are made possible without the need for manual input or oversight.
“Machine learning can be an enabling technology for the automation of 80 percent of” work activities that are potentially optimized via prescriptive analysis (McKinsey). This includes customer service, logistics & inventory management, hyper-specific content creation, and anything else that might benefit from real-time automated services.
So we’re using IoT tech (among other methods) to create data from all possible interactions both physically and digitally. That data is then being registered on blockchain distributed ledgers in a secure, transparent, and efficient manner. Lastly, machine learning is being used to parse that data in combination with automation to deliver real-time optimizations based on prescriptive analytics.
Placing Big Data in the Hands of SMBs
By housing data on a blockchain decentralized ledger using cloud-based software services, organizations can increase the volume and veracity of their analytics while decreasing risk, cost, and inefficiency. The transparency of blockchain systems combined with the speed & security of cryptographic transactions eliminates several inefficiencies associated with traditional business models. Highly competitive BI using this type of data-driven decision making is therefore accessible to businesses of all sizes and shapes.
This means that small to mid-sized businesses (SMBs) are able to remain competitive against larger organizations that have been attempting to make sense of big data for much longer. As the McKinsey Global Institute states, “organizations that are able to harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage.” And it’s interesting that the cost of adopting this technology is now far less of a factor than the willingness (or lack-thereof) to give it a shot.
What Does All of This Mean for Supply-Chains?
The more opportunities that are made for the digital registration of data via IoT and other sensor/tracking based technology, the better supply-chain data analysis becomes. This is because surpluses of data are now capable of being parsed by artificial intelligence and other automated systems. When it comes to machine learning and A.I., more data equals better pattern recognition which leads to improved optimizations. And supply-chains are quickly becoming primed for this specific type of technological advancement.
Automated optimizations that use machine learning to analyze data in real-time will enable participants within a supply-chain to maximize the efficiency of inventory management, transactions, and product development. Still, perhaps one of the more interesting evolutions will be the mutual benefit of information sharing that traditionally distinct and competing nodes within a supply-chain system will enjoy. As different organizations begin to understand how to interact in a truly transparent network, they will inevitably begin decentralizing, opening their sources, and ultimately sharing data with one another.
At the same time, individual entities can still use blockchain-based data-driven decisions to optimize their internal practices while remaining completely closed off from other organizations. And so the use-cases and availability of this strategic blend of BI tech will undoubtedly reach every corner of the supply-chain management industry.
How Will SupplyBloc Help SMBs Foster Data-Driven Decision Making?
SupplyBloc is a Blockchain-based Platform as a Service (PaaS/BaaS). The SupplyBloc platform enables businesses to create applications using our APIs, SDKs, and other development tools. These apps will have the ability to register data on a blockchain decentralized ledger, while also utilizing smart contracts and cryptocurrency for improved efficiency, transparency, and security of transactions, interactions, and exchanges. As a PaaS/BaaS, SupplyBloc will be easily integrated into existing supply-chain management systems that include IoT and other data tracking hardware, as well as previously established software.
SupplyBloc acts a key for SMBs to unlock and harness the power of big data analytics with blockchain technology at its foundation. Businesses utilizing SupplyBloc can create automated operations that offer real-time analytical optimization by combining machine learning, IoT, and our blockchain-based app development platform for the collection, organization, translation, and communication of data.
Learn more at supplybloc.io
