How can KM help you harness big data for better decision making?

Define the organization problems. Identify the root cause. Apply KM in providing insights from the available data which must be accurate. The insight gotten from the data can be reinforced with KM to make more informed decision.

Begin by leveraging KM to define precise business problems and goals, ensuring alignment with organizational strategies. KM aids in formulating key questions and identifying relevant metrics for robust analytics. It acts as a bridge, facilitating communication between data analysts and business stakeholders, ensuring insights resonate with strategic objectives. By integrating KM practices, organizations can harness big data effectively, transforming raw information into actionable intelligence for smarter decision-making.

KM aids in clearly defining the problem at hand and establishing specific goals for leveraging big data. This involves collaborating with stakeholders to understand their requirements and aligning data initiatives with organizational objectives, ensuring a focused and purposeful approach to decision-making.

Begin by defining the specific problems or opportunities where big data can make a difference in decision-making processes. For instance, in a retail environment, the challenge may be optimizing inventory management to reduce costs while meeting customer demand. Set clear goals related to these challenges, such as improving supply chain efficiency or enhancing customer satisfaction through data-driven insights. By aligning big data initiatives with specific problems and goals, you can focus on relevant data sources and analytics strategies to extract actionable insights and drive informed decision-making across your organization.

I used an example from a Project in the Historical Archives. In this Project, we harnessed big data by first defining our problem and goals: improving document digitisation processes. We aimed to determine how to prioritise documents and measure digitisation efficiency. By setting clear metrics like processing time and accuracy rates, we aligned our data strategy with our business goals and communicated this to stakeholders, focusing our analysis effectively.

In the big data journey, KM becomes a guiding compass. It assists in navigating diverse data sources by cataloging, classifying, and documenting them, ensuring a clear map for analysis. KM's role extends to establishing robust data governance and security policies, safeguarding the integrity of the insights gained. Collaboration is enhanced as KM facilitates partnerships with data providers and users. Moreover, it empowers organizations to tap into the wealth of external sources like open data, enriching the analytical landscape. In essence, KM acts as the backbone, ensuring that the process of harnessing big data is not just insightful but also structured and secure, driving better decision-making.

It is important to also vet data sources as a risk management, and adhere with GDPR and copyright rules that may be linked to some data sources. Different data sources have different policies and rules that needs to ensured. With the influx of information, it is important to create a parameter when engaging with data sources and create a pool recognized and verified data sources.

Identifying and accessing relevant data sources is fundamental in knowledge management. For instance, in a mobile company seeking to enhance customer experience, data sources could include customer feedback from mobile apps, call center logs, social media interactions, and market trend reports. Utilize tools such as data mining software and analytics platforms to gather and process this information effectively. By accessing diverse and reliable data sources, you can generate valuable insights on customer preferences, usage patterns, and market trends. These insights can then inform decision-making processes to improve services, develop targeted marketing strategies, and drive business growth effectively.

By leveraging KM, you can pinpoint and tap into the right data pools to inform decision-making. This involves identifying where valuable data resides, whether it's within internal databases, external sources, or user-generated content. Accessing these sources empowers you to make informed decisions based on robust data insights.

In the Historical Archives project (see above), we identified and accessed relevant data sources to improve digitisation. We catalogued and classified archival documents, establishing data governance and security policies. KM helped us collaborate with other departments and leverage external data, such as open data from public archives. This comprehensive approach ensured we had high-quality, structured data for analysis.

First, analyze your data. Break it down into manageable chunks, like customer behavior patterns or market trends. Then, use KM tools to organize and categorize this data. For instance, create databases or knowledge repositories specific to different aspects of your business. Next, visualize your insights. Use charts, graphs, or dashboards to display trends and correlations. For example, a sales team might use a heatmap to visualize customer interactions across different regions, helping them spot growth opportunities. By combining analytical tools with KM strategies, you not only make data-driven decisions but also empower teams to understand and act on complex information effectively.

You can analyse and visualise the data to improve digitisation. We can Excel for descriptive analytics and Google Cloud for predictive analytics. KM could guide you in selecting these tools and sharing insights from previous digitisation projects. Then you can create visual aids like pie charts and bar graphs in Tableau to illustrate processing times and error rates, effectively conveying our recommendations to stakeholders.

KM supports the analysis and visualization of big data, allowing teams to derive meaningful insights. By leveraging data analytics tools and visualization techniques, organizations can transform complex data sets into clear, actionable information, aiding in more effective decision-making processes

A complex spreadsheet won't win over stakeholders. KM helps you translate your data analysis into compelling visuals. Are we seeing customer growth trends? Sales fluctuations? Visuals transform numbers into stories with impact.

Pharmaceutical companies can leverage KM systems to aggregate data from clinical trials, drug development processes, regulatory filings, and adverse event reports. By integrating this data and applying advanced analytics, they can identify patterns, predict drug efficacy and safety, and optimize research and development efforts. This enables informed decisions regarding which drugs to prioritize for further development, which therapeutic areas to focus on, and how to enhance patient outcomes while adhering to regulatory requirements, ultimately accelerating the delivery of medications to market.

In a law firm, a KM initiative might include integrating AI insights into the existing legal knowledge base: - An AI tool analyzes contracts, extracting key clauses, definitions and recurring patterns. - These are linked to the firm's database, containing historical contracts and legal precedents. - This integration allows attorneys to access not only relevant documents but also metadata and AI-generated suggestions, like similar clauses used in different contexts. This shows you can really tap into your own existing knowledge and leverage it for future use.

People often waive around the term "data" like it has magical properties by itself. How many subject matter experts tell others "we have the data" to validate a point? Unless data is actionable and interpreted correctly, it might as well be lint or dust. Data points to something bigger than itself and the role of the Knowledge Manager is to weave that data into a coherent and meaningful narrative for its intended audience. For example, if all the data points to the fact that legal professionals "should" adopt Gen AI, what it the next step? Turn that "prescription" into a result by simply demonstrating what can be achieved in someone's workflow with the aid of the new technology - save some time, find me a valid answer to my research etc.

Take example of a retail store analyzing customer purchase patterns. KM integrates data from sales, marketing, and customer feedback systems. By visualizing this data through KM tools like dashboards, managers can identify trends like popular products or customer preferences. Acting on these insights optimizes inventory, marketing strategies, and customer experiences, improving overall decision-making processes.

In a Historical Archives project (see above), we interpreted and acted on our insights by validating our findings with archivists' experience and feedback. We used Google Cloud to refine predictions and stored insights in SharePoint. KM facilitated the integration of new insights with our existing knowledge base. We implemented process changes based on visualised data, reducing digitisation time by 20%, and regularly reviewed outcomes to learn and improve continuously.

In an M&A department, a KM system can revolutionize how data informs strategies and risk management. Implementing KM might involve the following steps: 1. By collecting and aggregating data from past transactions and due diligence reports, the KM system provides a wealth of information. 2. It then employs AI and machine learning to analyze this data, uncovering patterns in risks, commonly negotiated terms, and the success or challenges of previous deals. 3. This deep analysis can reveal critical insights, such as the contentious nature of specific clauses like earn-outs or frequently missed industry-specific risks in due diligence. 4. Consequently, this leads to more effective deal structuring and a streamlined due diligence process.

Consider implementing data governance practices to ensure the quality, security, and compliance of the data being utilized. Foster a culture of data-driven decision-making by providing training and resources for employees to enhance their data literacy. Implement robust data integration strategies to streamline the process of collecting and combining data from various sources. Additionally, continuously evaluate and update your technology infrastructure to accommodate evolving data needs and emerging technologies in the field of big data analytics.

Tames the beast: KM structures the chaos of big data, turning it into organized knowledge with clear context and purpose. Extracts the gems: KM acts as a skilled miner, uncovering hidden patterns and valuable insights buried within the data. Guides the hand: KM translates insights into actionable intelligence, empowering informed choices and strategic moves.